{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ad72755f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据基本信息:\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   页数      1000 non-null   int64 \n",
      " 1   类别      1000 non-null   object\n",
      " 2   彩印      1000 non-null   int64 \n",
      " 3   纸张      1000 non-null   object\n",
      " 4   价格      1000 non-null   int64 \n",
      "dtypes: int64(3), object(2)\n",
      "memory usage: 39.2+ KB\n",
      "None\n",
      "\n",
      "类别分布:\n",
      "类别\n",
      "技术类    336\n",
      "教辅类    333\n",
      "办公类    331\n",
      "Name: count, dtype: int64\n",
      "\n",
      "彩印分布:\n",
      "彩印\n",
      "0    648\n",
      "1    352\n",
      "Name: count, dtype: int64\n",
      "\n",
      "纸张分布:\n",
      "纸张\n",
      "双胶纸    615\n",
      "铜版纸    196\n",
      "书写纸    189\n",
      "Name: count, dtype: int64\n",
      "\n",
      "类别编码后分布:\n",
      "类别\n",
      "1    336\n",
      "2    333\n",
      "0    331\n",
      "Name: count, dtype: int64\n",
      "\n",
      "纸张编码后分布:\n",
      "纸张\n",
      "1    615\n",
      "2    196\n",
      "0    189\n",
      "Name: count, dtype: int64\n",
      "\n",
      "=== 线性回归 ===\n",
      "R²分数: 0.4786\n",
      "均方误差(MSE): 190.2254\n",
      "均方根误差(RMSE): 13.7922\n",
      "平均绝对误差(MAE): 12.0700\n",
      "\n",
      "=== 岭回归 ===\n",
      "R²分数: 0.4787\n",
      "均方误差(MSE): 190.1890\n",
      "均方根误差(RMSE): 13.7909\n",
      "平均绝对误差(MAE): 12.0678\n",
      "\n",
      "=== Lasso回归 ===\n",
      "R²分数: 0.4707\n",
      "均方误差(MSE): 193.1125\n",
      "均方根误差(RMSE): 13.8965\n",
      "平均绝对误差(MAE): 11.8771\n",
      "\n",
      "=== 决策树 ===\n",
      "R²分数: 0.8087\n",
      "均方误差(MSE): 69.7924\n",
      "均方根误差(RMSE): 8.3542\n",
      "平均绝对误差(MAE): 4.0575\n",
      "\n",
      "=== 随机森林 ===\n",
      "R²分数: 0.8889\n",
      "均方误差(MSE): 40.5294\n",
      "均方根误差(RMSE): 6.3663\n",
      "平均绝对误差(MAE): 3.3102\n",
      "\n",
      "=== 梯度提升树 ===\n",
      "R²分数: 0.8742\n",
      "均方误差(MSE): 45.9053\n",
      "均方根误差(RMSE): 6.7753\n",
      "平均绝对误差(MAE): 4.0076\n",
      "\n",
      "=== SVM回归 ===\n",
      "R²分数: 0.3195\n",
      "均方误差(MSE): 248.2639\n",
      "均方根误差(RMSE): 15.7564\n",
      "平均绝对误差(MAE): 13.2523\n",
      "\n",
      "==================================================\n",
      "模型性能比较\n",
      "==================================================\n",
      "        模型      R²分数       RMSE        MAE\n",
      "4     随机森林  0.888905   6.366269   3.310189\n",
      "5    梯度提升树  0.874169   6.775346   4.007553\n",
      "3      决策树  0.808692   8.354182   4.057500\n",
      "1      岭回归  0.478674  13.790903  12.067773\n",
      "0     线性回归  0.478574  13.792223  12.070004\n",
      "2  Lasso回归  0.470660  13.896492  11.877119\n",
      "6    SVM回归  0.319485  15.756393  13.252298\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Lenovo\\AppData\\Local\\Temp\\ipykernel_67000\\2910015835.py:139: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  plt.tight_layout()\n",
      "D:\\pppython\\Lib\\site-packages\\IPython\\core\\pylabtools.py:152: UserWarning: Glyph 178 (\\N{SUPERSCRIPT TWO}) missing from current font.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "特征重要性分析\n",
      "==================================================\n",
      "随机森林特征重要性:\n",
      "  特征名称       重要性\n",
      "0   页数  0.507501\n",
      "1   类别  0.415148\n",
      "2   彩印  0.043245\n",
      "3   纸张  0.034105\n"
     ]
    },
    {
     "data": {
      "image/png": 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u9PSKiopUV1enZ8+eufnmm9OjR4/suuuu7caz5BT3rbfeOltvvXV+/vOf5/nnn88222yTrbfeOnvssccH/p4B4IOIbgA+0ebMmZPZs2enR48e7b57ryPdF1xwQaZPn54jjzyy/Nl3vvOdjBw5MlOnTs3aa6/9geu97rrrsnDhwvLjxDbbbLP84Q9/yE033ZQjjzwy48ePL0dr9+7d88gjj2ThwoXl53pPnTo1LS0t+c53vlNe5qabbppJkybl2GOPzZFHHtnmGuWDDz44o0ePzqhRo3LiiScmSc4888xss802mTBhQjbYYIPsueeeOeecczJixIjMnz8/zc3N7U4vv//++9PY2Jj/+I//yMCBA3PTTTflhBNOWJ4f+Xuqr6/P6NGjM3To0AwdOjRjx47NXXfdVX6/5Kj8kqPsyTt/GJk1a1bmz59fvtnbV7/61Wy88cZ54403snjx4ixevDgnnXRS1llnncydO7c83Zw5c7L//vu/75jOPvvsLFq0KHPmzMk555yTjTfeuHwjuaVdsw4Ay0t0A/CJtXDhwvzTP/1TBg0a1OYa5eSdO10vOdI9ffr0nHnmmamsrExlZWU22WSTnHbaaUu9c/WDDz6YrbbaapnW/+qrr+aUU04pP4f6uuuuyxe+8IW8+uqrOe2008rXW//gBz/IQQcdlLq6uvK8W2yxRR577LHyNdRLxjxq1Kj0798/P/rRj9qt77LLLssBBxyQ9ddfP8k7z8F+8skn8+CDD2aTTTYpT1dRUZGZM2dmk002yauvvtpmGUue/73kGeCjRo36SJ5T3dLS0i7yDzrooOy3335tPiuVSm3+qFFVVZW+ffume/fu6dy5c771rW/l7rvvTv/+/XPbbbfllVdeyf3335+f/exnuf7669OrV6907tw53bp1S+/evZd6fTgAfJzckhOAT6xu3brl2WefTZ8+fZK8c01z8s6p2/369cuXv/zlJEmnTp1y77335ic/+UkqKyuz6667lk9Hfvdds8eNG5fx48eX7xr+jy699NJ89rOfzciRI/PSSy9lp512yq677po999wzF154YS699NL84Q9/yG233ZZHHnkkDz/8cMaOHZujjjoq48ePz3bbbZfbbrst66yzTpKUb/y1ZAxdunTJ73//+zQ1NS31uvKqqqrccMMN5Uju06dPedv/0VprrZUFCxbk0ksvXWrAf9S22267TJgwIZ07dy7fhG7evHlJkgEDBpSnW7hwYRYvXpxXX321zedL/OIXv8jo0aPz5JNP5r777ssxxxyTUqmUzTbbLIsWLUpjY2NqamoK3x4AWFaiG4BPpObm5iQpR+eECRMyatSorLfeetlkk02y4447ZrPNNsv999+ffv365cILL8yCBQty6623lu9UnvzfEdq5c+fmsMMOy4EHHpi11lqr/H11dXX55mPXX399tthii4wcOTL33HNP1l133Vx11VW54447yo8ae/jhh3PhhRfmvvvuKx/FbmlpyQ033JAjjjgihx9+eB544IE24fjuR3C9+5Tyd49viWU9Kl1VVZXvfve7S32019K0tLSktbU148ePT21tbVpbWzNz5sxMmjQp8+bNy6JFizJp0qSUSqU0Njamvr4+m2yySaqrq/Pmm2/m7rvvbhfD3/zmN9Pc3Jxrr7223boaGhpSX1/f7kh1586d06dPn1RVVWXy5Mn57Gc/m+OOOy7jxo3Lvvvum8bGxuy0004ZNWpUdt9996y22mpt5l9yND1J+eZxS8a1cOHCNDc3Z/bs2Un+747pa6yxxjL9jABgaUQ3AJ9IS56HvcSYMWOyyy675NJLL83cuXNz22235fHHH8+tt96aadOmZe7cuWloaMgXv/jFcnS3tLSUA/Chhx5KRUVFu0dhbbXVVunSpUv69++f1VZbLZdcckmS5Nhjj80xxxyTioqKckAmyfe+97185StfyVZbbVV+/Fd9fX26d++eK6+8Mk1NTeVTu5doaGh4zziur69vcw30e2lsbGz3MznqqKOSJDfccENefPHFzJo1q90N05ZoampKU1NT+S7iVVVVOf/883P++eeXp9liiy3K0d3Y2JiXX345nTt3zuDBg1NTU5Oampql/lHgH0/jb21tTWNjY2688cZ87Wtfy0MPPZRDDjkkjY2Neeutt1JbW5uddtop5557bvbcc89UVlbmiCOOSHNzc/7whz/k8ssvzyGHHJJvf/vb5Wvql/az3H///fPHP/4xnTp1ajOuJeNpbGzMrrvumnvuuecDf74A8F4qSu8+bw4APiFqampy//3354tf/GKSd+K0qqqqXdAuj+bm5lRXL//fqxsaGrJ48eL06tWrw+v+sA4//PAsXLgwN998c7vvTjnllFx55ZUZNWpUfv7zny91G6uqqnLxxRfnmGOO+TiG286ll16a/v37Z911183666//gc9JX3IJwbvvsJ4ka6yxRk477bQcc8wxy/T7bGlp+cB1AcD7Ed0A8CnX0T8mAAAfTHQDAABAQTwyDAAAAAoiugEAAKAgohsAAAAKIroBAACgIKvsrUpbW1szY8aMrLbaakt95icAAAAUpVQqZf78+Rk0aFC7R1S+2yob3TNmzMjQoUNX9DAAAAD4FJs+fXqGDBnynt+vstG92mqrJXlnA3v06LGCRwMAAMCnSV1dXYYOHVpu0/eyykb3klPKe/ToIboBAABYIT7ocmc3UgMAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoSPWKHsCHteEZ96WytuuKHsbHYtqY3Vb0EAAAAFgOjnQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAURHQDAABAQUQ3AAAAFER0AwAAQEFENwAAABREdAMAAEBBRDcAAAAUpEPRfdddd2XkyJEplUpJkilTpuSJJ57I7bffnvPOOy+77rprtttuuzQ2NiZJFi1alNbW1jbLGDt2bLbYYovy+5dffjkzZ87s6HYAAADASqe6IzPdeOONOeSQQ1JRUZEk2X777fOFL3wh66yzTi688MLccccdWWONNdLQ0JCqqqqsvfba6dKlSyorKzN16tRMnjw5NTU1qampKS/z6KOPTo8ePXLjjTd+NFsGAAAAK9hyR/fs2bMzadKk3HTTTfmf//mfbLTRRuncuXN++tOfZsCAAbnwwguz5557tpnn3Uew+/btm86dO6eqqiqVle8caL/zzjszbty4/PWvf/2QmwMAAAArj+U+vfz73/9+jj766DQ3N+eb3/xm7r///lRXL1+7LzktPUleeOGFHH744bnhhhsyaNCg5R0OAAAArLSWK7pnzJiRq6++OhdffHE22mijbL755jn88MPbTVcqldLS0lK+pvszn/lMBg8enGHDhqV79+5paWkpTztx4sT84Ac/yM477/whNwUAAABWLst1iHrQoEGZMGFCKioqsscee+SKK64of7fNNtukuro6FRUVGTZsWFpaWrLbbrvll7/8Zd56661MmDAha621VpLkRz/6UcaPH58pU6bkoYceyuuvv55XXnklDQ0NWXfddXPMMce0W3dDQ0MaGhrK7+vq6jq6zQAAAPCxWO5rugcPHpzdd989//Vf/5UFCxbkrbfeSpI89thjGTZsWAYPHpynn346ffv2TZI0NjZm4cKF+fKXv5yqqqokyd/+9rc89NBDmTt3bs4///wMHz48r7766vuepn7eeeflrLPO6sg2AgAAwAqx3Nd077vvvhk/fnxGjx6dAw88MM8880yb79dee+08//zz5fc1NTUplUqZOnVqnnnmmUyfPj21tbXl77t06ZIRI0bkgQceeN/1nnzyyZk3b175NX369OUdOgAAAHyslju6zzvvvPzlL3/Ja6+9lieeeCL7779/m+//5V/+JY888shS5120aFFWX331dp9/5zvfyfe+9700NTW953pra2vTo0ePNi8AAABYmS13dG+88cZZsGBBLrzwwuy88845+uijUyqV8thjj+XYY4/NXnvtlWuvvTatra257bbbsmjRovK8M2fOTP/+/dss75577smgQYMyZMiQ7LfffuWbrwEAAMCqbrmie8GCBRk4cGC+/e1vp6GhIWeccUZ++MMf5pVXXskRRxyRbbbZJltvvXV69eqVMWPG5JBDDsn06dOzcOHCXHrppTnnnHMyfPjwJMkbb7yRcePG5eijj05ra2tuvvnmTJs2LZtttlnmzZtXyMYCAADAx2m5bqTWvXv3PP/88+nXr1/5sylTpqRXr1655ZZbst122yVJLrnkkmy//fbZfPPN87nPfS5J8qc//akc7Ekyd+7c7L///vnFL36Rbt26JUkef/zxPPjgg+nZs+dHsnEAAACwIlWUSqXSh13IwoULy+G8xIwZM7LaaqtltdVW+7CLX6q6urr07NkzQ4+7NZW1XQtZx8pm2pjdVvQQAAAAyP816bx58973nmPL/ciwpfnH4E7eeaY3AAAAfJot943UAAAAgGUjugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAAClK9ogfwYU08a+f06NFjRQ8DAAAA2nGkGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAAClK9ogfwYW14xn2prO26ooexSpg2ZrcVPQQAAIBPFUe6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAKIroBAACgIKIbAAAACiK6AQAAoCCiGwAAAAoiugEAAKAgohsAAAAK8qGju6WlJfPnz/8oxgIAAACfKNXLM/Ef/vCHvPXWW5k5c2amTZuWiRMn5qmnnsp2222XO++8M0lSKpUyevTofOlLX8ro0aPL886YMSMPPvhg7rvvvlx66aXp3r37R7ohAAAAsLJZriPdt912W6666qpMnjw5nTt3zoknnpg///nPOe6445K8c9T7m9/8ZsaNG5dtttkmSXLGGWdkzTXXzI477pjbbrstQ4cOzWuvvZazzz47Xbt2Td++fdO3b9906tSpHO4AAADwSbBcR7o32mijfPOb30ynTp2yzz775Nxzz80TTzyR3XbbLc8880y++93vZu7cuXniiSfSu3fvJMmbb76Zb3zjGznzzDPbLKumpibHHntsxowZkyTZZpttUltb+9FsFQAAAKwElutId0tLS77//e9no402yrx58/Lyyy/n/PPPzymnnJIbb7wxra2t+f3vf5/a2tq0tLQkSaqqqpa6rOrq9r2/tM8AAABgVbVclXv44YfnySefTGtra4477rhUVlZmgw02yIknnpiampo0NzcnSXbbbbcMHDgwv/rVr5IkF198ca699trU19dn0aJFmTp16ke/JQAAALCSWa4j3ZWVlbnuuutSVVWVG264IfPmzcsPf/jDlEqlVFRUpLW1Nfvvv3/eeOONnH/++eX5jjvuuEybNi2vv/566urq0qdPn+UeaENDQ+rq6tq8AAAAYGW2zNHd3NycLl26pLa2Np06dcqkSZOy6aabpnPnzunSpUsmTJiQ7bffPvPnz8+jjz6aAQMGfKQDPe+889KzZ8/ya+jQoR/p8gEAAOCjtszRXV1dnebm5jQ0NOSf//mfkyR33nln/va3v6WhoSHHHntsdtlll9x7773p3LlzHn/88XbLePPNN3Pqqad2aKAnn3xy5s2bV35Nnz69Q8sBAACAj8tyXdNdVVWVn/zkJ6mqqsqIESPyyCOP5NBDD81vf/vbPPTQQ6moqEhjY2P23XffjB8/Ps8880xKpVIWLFiQH/7wh7nooovy5S9/OfX19eVlvvLKK7nyyiszYcKE9312d21trbubAwAAsEpZruh+9NFHc/bZZ2fcuHEZNWpUDj300Hz2s5/NrrvumhdeeCELFy7MqFGj0qlTpzz99NPp379/xo8fnz//+c/Zfffd89hjj2XDDTdMkpRKpSTJ4MGDM2nSpJx++unZfPPNP/otBAAAgBVkmaP76aefzj777JNrrrkm6623Xpqbm9PS0pLDDjssm222Wa644oqMGTMmRx99dE4//fTyo8LOOuusVFVVZccdd2yzvKampiTvHD2/9dZbP8JNAgAAgJXDMkf3RhttlLFjx2abbbZJ8k40NzY2JkmeffbZ3HHHHXnwwQfz+c9/vs18u+yyy1KXt+TxYgAAAPBJVVFacp73Kqauru6du5gfd2sqa7uu6OGsEqaN2W1FDwEAAOATYUmTzps3Lz169HjP6ZbrOd0AAADAshPdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQkOoVPYAPa+JZO6dHjx4rehgAAADQjiPdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQkOoVPYAPa8Mz7ktlbdcVPQwAAFilTRuz24oeAnwiOdINAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAF+ciju7W19aNeJAAAAKySlju6W1pa8r3vfS+zZ89Okpx33nk5/PDDkyTz5s3LP/3TP+V///d/y9NffvnlqaysTHV1dfnVv3//dsvdZ599csUVV3R0OwAAAGCls9zRXVlZmbfeeiu77LJL6urq0qlTp3Tp0iVJctRRR2XYsGFZd911y9NXV1fn61//epqbm9Pc3JxJkyalU6dOSZK7774722yzTZKkpqam/DkAAAB8EixXdC9evDhNTU257LLL0rt377z88svl76ZPn54XXnghN954Y0qlUhYvXpwkqaioaLecJZ9VV1enqqrqw4wfAAAAVlrVyzPxgQcemCeeeCLdu3dPknzpS1/KnDlz0rNnz/zud79LRUVFNtlkkzQ1NWW99dbLfffdl+bm5vz2t7/NOuuskyRpampKU1NTknfie2lRDgAAAJ8EyxXdY8eOLf/3Cy+8kJ133jmbbLJJPve5z+Uzn/lMHnvssVxyySX5zGc+U55u//33z4477pif/vSn6d69e/7t3/7N0W0AAAA+FZb7mu7GxsZccskl2XbbbXPBBRdkxx13TFNTU4499tiMGDEiG2+8cS677LKUSqW0tLSkVCplwIABqa+vT2VlZQYMGJB+/fqVj3Yvq4aGhtTV1bV5AQAAwMpsuaL7zjvvzLBhw/LAAw/kiSeeyKhRo9KlS5esvvrq6dy5c84///z893//d37wgx/kt7/9bSZNmpR+/fplwIABufrqq3PRRRdlwIAB6d27d4444ojlGuh5552Xnj17ll9Dhw5drvkBAADg47Zc0b3VVlvljjvuyJ133pnhw4cnSbp27Zo//vGP5Wm+9KUvZdKkSRk5cmQ22GCDNDQ05O23386mm26am2++OW+//XYuuuii1NTUpLW1NS0tLcu07pNPPjnz5s0rv6ZPn748QwcAAICP3XJd0/2Vr3wlM2fObPNor9dffz0NDQ0ZNmxY+bOWlpY0NjbmhRdeSI8ePTJr1qxMnDgxm266aZKkvr4+Xbp0SUNDQ+rr65dp3bW1tamtrV2e4QIAAMAKtVzRPX78+DbvX3jhhWy99dbZcMMNs9122+XMM89sN0+pVMrxxx+fHXbYIYMHD07yTnR37do1e++9d/bee++Ojx4AAABWYst9I7Ulxo8fn9122y1jxozJXXfdlQcffDD/+q//mjfffLM8zaxZszJq1Kg8+uij+a//+q+89dZb+eUvf5nbb789gwYNSpK8+uqrueOOO/L000+nW7duH36LAAAAYCWxXEe6n3322Tz55JO57bbb8sILL+Tiiy/OXnvtlST5wx/+kNNOOy3rrLNOdt999+y2224ZMmRIXn311Tz22GMZMmRImpubc/PNN2fHHXfM6NGjkyR1dXU56qijsu2222bkyJEf9fYBAADAClNRKpVKyzrxsccem+nTp2efffbJfvvtl5qamnbTvPTSS/nFL36RqqqqXHDBBR/pYN+trq7unbuYH3drKmu7FrYeAAD4NJg2ZrcVPQRYpSxp0nnz5qVHjx7vOd1yRffKRHQDAMBHR3TD8lnW6O7wNd0AAADA+xPdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFCQ6hU9gA9r4lk7p0ePHit6GAAAANCOI90AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFqV7RA/iwNjzjvlTWdl3RwwAAAOBDmjZmtxU9hI+cI90AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFCQ5Y7uxYsXp7W19QOna2lpyeLFizs0KAAAAPgkWO7oHjFiRGpqatK5c+fyq7q6Op06dWrzWW1tbbbddtskyeWXX54pU6bkuuuuy+677576+vp85zvfSZIcddRRueqqqz7arQIAAICVwHJH95QpU9Lc3Jz6+vry65BDDsnPfvazNp81NzfnySefTJL07t07++23Xzp16pROnTrlN7/5TebMmZMkefTRRzNo0KCPdqsAAABgJbDc0V1VVdXusxkzZiw1nKuqqtLY2JiKiorss88+ee655zJ79uxMnjw566+/fp577rlMnjw522+/fZKktbU1DQ0NHdgMAAAAWPlUd2SmRx55JIceemj5/euvv55nn302xx13XPmzK664IjvuuGMaGxtz1113pa6uLnfccUfWXnvtvPnmm5kxY0Zmz56dTp065fOf/3zq6+szZ86cHHzwwbniiis+9IYBAADAitahu5e3tLSkubk506ZNy7Rp01JfX5/XX3+9/D5JSqVSkqR79+4599xzM2PGjIwaNSojRozI5MmT86Mf/Sh//etf8+1vfzsvvvhirrzyymy//fbvGdwNDQ2pq6tr8wIAAICVWYeiu7Ky7Wxf/vKXc+mlly51mokTJ2brrbfOqaeemq9//evp2rVrvvWtb+WUU07Jgw8+mBdffDFJMnPmzAwePPg913neeeelZ8+e5dfQoUM7MnQAAAD42HTo9PJ/9Morr2SttdZa6ncjRozIuHHj0qNHj6y//vp59NFHM3To0CxcuDADBgzIjTfemCSZOnVqPve5z73nOk4++eTyHc+TpK6uTngDAACwUutQdL/7Od319fWZOXNmttpqqzbTNDc3J0mefPLJfP3rX8+iRYtSKpXypS99KW+//Xb+5V/+Jb/97W/z0EMP5amnnsqjjz6aH/7wh++5ztra2tTW1nZkuAAAALBCdOj08qamplRXv9Prt912WzbZZJOsttpqeeONN5Ikm2yySbp27Zok2WqrrfLqq69mvfXWy913350XX3wxW2yxRQ455JAkyWGHHZYTTjghzz//fDbffPOPYpsAAABgpdChI90777xzpk2blieffDLHH398rrvuukycODHbbbddDjzwwFx++eXp169fefoZM2Zk9dVXz1FHHZXPfvaz+fOf/5ybbropSXLQQQfl29/+dv7jP/4jNTU1H81WAQAAwEpguY90L168OA8++GBGjx6dHXfcMT/72c/yla98JRtuuGEmTZqU+vr6rLfeernqqqvK8wwaNCi//e1vc9JJJ2XcuHHZfffds9122+Xtt9/O6NGjs+WWW+ZXv/pV7r///o904wAAAGBFWq7oLpVK2WGHHbL//vund+/e+dvf/pYDDzyw/H3//v1z5ZVX5pZbbsnZZ5+d559/PgsWLMiFF16YzTbbLGPHjs2TTz6Zq6++OkcccUQ+//nPZ/jw4XnggQdy1VVXZZ999sn555//kW8kAAAArAjLdXp5RUVF7rvvvnTr1i1VVVXvOd3IkSPz4osvplOnTkmSPn365MYbb2xzd/KhQ4fm7rvvzsYbb5wk2XvvvfP444+nV69eHdgMAAAAWPks9zXdPXr0WKbplgR3knzjG99o9/2uu+7a7rPPf/7zyzscAAAAWGl16O7lAAAAwAcT3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQkOoVPYAPa+JZO6dHjx4rehgAAADQjiPdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFAQ0Q0AAAAFEd0AAABQENENAAAABRHdAAAAUBDRDQAAAAUR3QAAAFCQ6hU9gI4qlUpJkrq6uhU8EgAAAD5tlrTokjZ9L6tsdM+ZMydJMnTo0BU8EgAAAD6t5s+fn549e77n96tsdPfu3TtJ8sorr7zvBsLKpq6uLkOHDs306dPTo0ePFT0cWGb2XVZV9l1WVfZdVlWfln23VCpl/vz5GTRo0PtOt8pGd2XlO5ej9+zZ8xP9i+STq0ePHvZdVkn2XVZV9l1WVfZdVlWfhn13WQ4Au5EaAAAAFER0AwAAQEFW2eiura3NGWeckdra2hU9FFgu9l1WVfZdVlX2XVZV9l1WVfbdtipKH3R/cwAAAKBDVtkj3QAAALCyE90AAABQENENAHwqzZgxI48//njmz5+/oocCwCfYShvdEydOzBe+8IWsvvrqOfHEE7Msl54/8sgjGTFiRPr27Zuf/OQnH8Moob2O7LtJ8uKLL6Z3794Fjw7eW0f23csvvzwDBw5Mp06dstNOO2XmzJkfw0ihrY7suxdddFE22GCDHHHEERkyZEgeeeSRj2Gk0FZH/82QJE1NTdloo43y8MMPFzdAeA8d2Xe/+tWvpqKiovwaOXLkxzDSlcNKGd0NDQ356le/mk033TTjx4/P3//+91x77bXvO8+sWbOyxx575IADDsgTTzyRG2+8MQ899NDHM2D4/zqy7ybJ1KlTs9tuu2Xu3LnFDxKWoiP77mOPPZbTTz89119/faZOnZr6+vqccMIJH8+A4f/ryL47efLk/PjHP87f//73TJgwISeccEJ+8IMffDwDhv+vo/9mWOKCCy7IxIkTixsgvIeO7rtPP/10nnvuucydOzdz587NnXfeWfxgVxalldDYsWNLq6++emnhwoWlUqlUevbZZ0tbb731+87z05/+tPS5z32u1NraWiqVSqU77rijdNBBBxU+Vni3juy7pVKpNGLEiNIFF1xQWkn/J8mnQEf23SuvvLJ0++23l99fffXVpXXXXbfQccI/6si++7e//a101113ld/feeedpY022qjQccI/6ui/GUqlUmny5MmlXr16lYYNG1Z66KGHChwltNeRfXf69OmlAQMGfBzDWymtlEe6//rXv2aLLbZI165dkySf//zn8/e///0D59lhhx1SUVGRJNl8883zl7/8pfCxwrt1ZN9NknvuuSf77rtv0cOD99SRfffwww/PPvvsU37//PPPZ5111il0nPCPOrLvrr/++vnqV7+aJFmwYEH+8z//s82+DB+Hjv6bIUn+4z/+IyeddFLWXHPNIocIS9WRfffPf/5zWlpaMmTIkHTr1i3777//p+oMz5Uyuuvq6jJ8+PDy+4qKilRVVb3vL+Yf5+nRo0dee+21QscJ/6gj+26SrLXWWkUPDd5XR/fdJebMmZNf/vKXOfLII4saIizVh9l3/+d//icDBw7M66+/nlNPPbXIYUI7Hd13r7nmmsybNy/f/e53ix4iLFVH9t3Jkydn0003zX333Zfx48dn2rRpOeWUUz6O4a4UVsrorq6uTm1tbZvPOnfunEWLFi3zPB80PRShI/surAw+7L575JFHZquttspuu+1WxPDgPX2YfXennXbK7373u1RXV+d73/teUUOEperIvjtr1qycfPLJueqqq1JdXV30EGGpOrLvnnTSSfnd736XDTbYICNGjMj555+f2267reihrjRWyuju3bt3Zs2a1eaz+fPnp6amZpnn+aDpoQgd2XdhZfBh9t2rr746f/zjH3P11VcXNTx4Tx9m362urs4222yTn//857nmmmuKGiIsVUf23eOOOy6HH354/umf/qng0cF7+yj+vdurV6/Mnj07DQ0NH/XwVkorZXR/4QtfyLhx48rvp02bloaGhvd9nNI/zvPss89m8ODBhY4T/lFH9l1YGXR03/3zn/+c4447LjfffHPWWGONoocJ7XRk3/31r3+diy66qPy+uro6VVVVhY4T/lFH993//M//TK9evdKrV6889thj2X333TNmzJiPY8iQpGP77qhRo9rM89RTT2XAgAHtjph/Uq2U0b3ddttl3rx5ue6665IkY8aMyciRI1NVVZW6uro0NTW1m2ePPfbIY489loceeijNzc258MILs/POO3/cQ+dTriP7LqwMOrLvvvHGG/nqV7+a73//+9l0002zYMGCLFiw4OMeOp9yHdl311tvvZx55pkZO3Zspk2bljPOOMPNLPnYdWTfnTp1aiZMmJBnn302zz77bDbbbLNceeWVOeKIIz7u4fMp1pF99/Of/3yOP/74PPnkk7nnnnty+umnf7ruA7Oib5/+XsaOHVvq0qVLqX///qU+ffqUJk6cWCqVSqU111yzNHbs2KXO84tf/KLUqVOnUt++fUtrrrlm6fXXX/8YRwzv6Mi+WyqVSlOnTvXIMFao5d13f/rTn5aStHvBx60j/3f3+uuvLw0bNqzUq1ev0je/+c3yo2/g49TRfzMssf3223tkGCvE8u67jY2NpcMOO6y02mqrldZee+3SWWedVWpqavqYR73iVJRKpdKKjP7389prr2X8+PHZaqut0q9fv2Wa58UXX8z//u//Zvvtt0+PHj0KHiEsXUf2XVgZ2HdZVdl3WVXZd1lV2XeX3Uod3QAAALAqWymv6QYAAIBPAtENAAAABRHdAAAAUBDRDQAAAAUR3QCwCnr22WcLX0dzc/NSn7e6rNPOmDFjmecHgE8q0Q0AK9i2226bsWPHtvnswQcfzNZbb73U6f/0pz/ln//5n/PHP/4x8+fPz+LFi1NfX9/mtXjx4ixYsOB911sqlTJ79uw89dRTufbaa3PDDTe0+f6yyy7LBhtskA033DAbbrhhBg8enNVXXz0bbrhhBg0alL59+5a/22CDDTJmzJg2848YMSKTJ09ut96RI0fm2muvXYafDACs+qpX9AAA4NPqnHPOyZe+9KV07do1vXr1SpKcffbZ+dWvfpWGhobMmTMn66yzTlpbW3PwwQfn7LPPzvz583P44YfnxBNPzHbbbZeNN944L7zwQiorK9PQ0JCKiorU1NSktbU1n/3sZ/PXv/41yTtHnffff/8sWrQoixYtSlNTU6ZPn56hQ4dmzTXXzBprrJHBgwdn4cKF6datW5Lk6KOPztFHH10e72WXXZZx48bl2muvzSWXXJJp06blwgsvfM/tq62tTefOnZMkEyZMyBFHHJHHH388Xbp0SU1NTUE/VQBYuYhuAFhBbrrppnzlK19JRUVFKioqkiTf//73c/LJJ+fpp5/OSSedlIcffjitra1pbm7OwoULs88+++Qzn/lMzj333CQpR3WSjB49OkOGDMk555zTZj3Nzc3p169frrrqqvTs2TO9e/dOdXV1hg0blptuuimbbbZZedrW1tbU19eXY/n222/P97///SRJXV1d6uvr89hjj2XevHlpbm7OHXfckST513/915xxxhlJkoaGhtTU1KSysjIPP/xwSqVSnnvuufTu3TtJUlVVlcrKd062a2pqSnV1dXn7AeCTRnQDwAqwePHiTJ48OTvssEMWLVqURx55JPvss09uvPHGdtNWVlampqYm999/f1555ZWMGzcu//7v/54vfelLOfjggz9wXffcc0+OPfbYdkeXX3vttey9996pra0tf9bS0pK11147v//978vj3HDDDXPHHXe855HuM888M/Pnzy8v4+c//3lmz56dJHnmmWfy3HPPpaqqKo888kj69u2b+fPn58EHH8zRRx+d+vr6PP/88xk8eHCHfo4AsLIT3QCwAjz11FPZeuut8/DDD2eXXXbJiSeemK233jr9+vVLly5d0tTUlLlz52bYsGFJksbGxlx00UV5+umn88QTT+S+++7LCSec8J7LL5VKaW1tTVVVVfbaa6/stdde+dvf/pbnnnuuPM0xxxyTb33rW1lrrbWSJJ06dcrXvva1NstZ1iPQ755uypQp2XTTTZMkBxxwQE444YTU1dXl3nvvzdZbb5299tor+++/f/bff/9lWjYArMrcSA0AVoDq6uoccMAB5fdVVVXp3Llzpk2blmnTpmXmzJmpr6/P6NGj881vfjPTpk3LqFGjMmPGjBx88MH5wQ9+kBEjRuSWW27JLrvsUl7OT37yk/Tt2zfdunXLt771rTbrvOeee3LllVeWb7ZWKpXS2NiY+vr6vPrqqzniiCPajbNbt2558cUXs+GGG+aSSy7JAw88kA022CCXXXZZ7r333my44Ya57bbbsvrqq5fn+etf/5oRI0YkSQYPHpwLL7wwdXV12XLLLT/qHyMArPQc6QaAFWCrrbbKVlttVX7/u9/9Lo899lhOPPHEpR5drqyszMsvv5yRI0emurq6fE10kixcuLD839/5znfaXdO9RE1NTcaPH59p06YlSebNm5errroqtbW1aWxsTFVVVZvpGxsbs+eee2avvfZKkixatChf+MIXcswxx7QL9KampsyZMyd9+vTJTjvtlI033rj83QYbbJBf//rXaWxsLF8rnqQc/e8+vR0APmlENwCsAI2NjXnqqady++23Z9y4cWloaMjJJ5+cbbfdNi+++GI5qhctWpQkufDCC/PjH/84u+++e2bMmFFeTlVVVbtYfr91rr/++tljjz2SJOeff3722muvDB48OG+88Ua7R4ZtuummmTdvXnks06dPz+qrr54xY8ZkzJgxmTVrVmpqatKzZ8+0tramoaEhb7zxRs4666w2y+nVq1eamprSp0+fdOnSpXxN95FHHpmuXbvm1Vdf7dgPEQBWAaIbAFaAWbNm5ZRTTsnXv/71rL/++jnjjDPyxS9+MV/84hfz+OOP54tf/GKS5Mwzz0x1dXVOO+208pHh/fbbr0Pr3GOPPbL22muX33fq1ClDhw7NWmutlXXWWae8ziXeff33RRddlGuuuSZ/+tOf0rNnz7z00kvZZptt8vDDD2fdddf9wHXvsMMO5SPyrukG4NNEdAPACjB48OA88sgjSZK77767/HmpVMro0aPzq1/9Kttvv32beSoqKjp8KvaUKVOy1157pUePHuVl1NXV5de//nW6d++eJJk/f37uuOOOXHPNNW3mnT17diZOnJiWlpZsttlm2WGHHfLwww/n7LPPXqbgBoBPM9ENACtYqVRKqVRKktTW1ubqq69Onz59kiQLFixoc5Oy5J3TxJubm7No0aK0trYudZmtra3l66UrKiqy9tpr5/nnn28zzbBhw3LllVe2eU730vTt2zfXXHNNpkyZkl/+8pe56qqrst122+W0007L2LFjs9tuu2XvvffOwIED28zX3Nzc5rVkLO/W1NSU1tZW13UD8IklugFgBVu0aFEaGhrK73fYYYfU19enX79+6d27d7trrRsaGrJ48eL069cvtbW1qaysTN++fcvfX3bZZWlubk5DQ0OeffbZvP3229lnn33Ss2fPNs/qXrBgQQ455JB2n2200UYZO3Zs/v73v+eYY45JfX19Jk2alEGDBuXAAw/M5MmT06dPnzQ0NOSBBx7I1VdfneOPPz633npr9txzz/KympqaUl9fn4ceeiiHHnpounTpUo7u0047LaeddloaGxuz//7754ILLvjIf64AsDKoKC350zoAsFJpaWlZ5pukFeXWW2/NwIEDs8EGG6R3797vOd1bb731vt8DwKeV6AYAAICCVH7wJAAAAEBHiG4AAAAoiOgGAACAgohuAAAAKIjoBgAAgIKIbgAAACiI6AYAAICCiG4AAAAoiOgGAACAgvw/w3o+OP1ahZ8AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "梯度提升树特征重要性:\n",
      "  特征名称       重要性\n",
      "0   页数  0.490702\n",
      "1   类别  0.447187\n",
      "2   彩印  0.041615\n",
      "3   纸张  0.020496\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "线性回归系数分析:\n",
      "  特征名称        系数     系数绝对值\n",
      "1   类别 -9.380922  9.380922\n",
      "2   彩印  7.337378  7.337378\n",
      "3   纸张  3.248686  3.248686\n",
      "0   页数  0.132449  0.132449\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "==================================================\n",
      "最佳模型: 随机森林\n",
      "最佳R²分数: 0.8889\n",
      "==================================================\n",
      "最佳模型的前10个预测结果:\n",
      "   实际价格       预测价格      绝对误差\n",
      "0    75  72.000000  3.000000\n",
      "1    84  83.231905  0.768095\n",
      "2    68  68.960000  0.960000\n",
      "3    90  89.980000  0.020000\n",
      "4    85  84.366333  0.633667\n",
      "5    40  37.040000  2.960000\n",
      "6    40  37.043333  2.956667\n",
      "7    64  63.805000  0.195000\n",
      "8    56  54.120000  1.880000\n",
      "9    72  74.740000  2.740000\n"
     ]
    },
    {
     "data": {
      "image/png": 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cxueff4558+bhlFNOwe7duwFEf+FLSUlBfX09KioqUFZWhvvuuw/Z2dkoLS1tMdJhZWUl/v73vycUOo8xGo349NNP8fjjj+Oiiy6C1+vFnDlzcNppp+Gggw7q0PYhIiLqb8aNGwev14vDDz8cuq6joqICBQUFuPfeexEMBiFJUkIWzUUXXYSioqJWf9AyGAzxv7UbN25EaWkpdu7cGb8WWbRoEXbu3IkdO3Zg8+bNOOmkkwAAd999N4qLi5GXlxdflslkgiRJePvtt+M/CsYeqamp7f5BDYhmWjkcDixZsiRh+lFHHZVwjbdy5UoceeSRsNvtyMrKwpw5c+I/LrZHMBiE1WrFW2+9hRtuuCEhi8xiseDiiy9GMBjEDz/8gJ9//hnBYBC5ubmwWq0AooPRXHzxxbj44ovx4IMPJizbaDS2K7N93bp1mDt3LkaOHJkw3WKx4LzzzmuxLa+55poW21IIgVmzZmHlypWYO3cuGhoaWowkXVZWBpfLhUWLFuHoo4/GJZdcglNPPRU1NTUJy1q5ciVuv/12PPPMMxg2bFh8XTh4DFHrGJQiGuDuuusulJSUIBKJ4P333wewJ619586dSE9PR3p6Ok444YQW7/33v/8dH33vhBNOwIoVK7Bw4UK43e52/erjcrlgt9vh9/vx5ZdfYu7cuaiqqkJ1dTUyMzPhcrlQVVWFu+66C263G8FgEHl5eWhoaEhoQ319PXJycjBjxoz46HUAcP311+P8889HVlYWzj//fGzatAmPPPJIh7bPc889h++//77VR6zb4758+OGH+Ne//oXbb78duq5j06ZNkCQJQ4cOxddff41nn30W9fX1uOmmm5CSkoLLL78cP/zwA4455ph4ttmZZ56JlJQUOByO+OPGG29EeXk50tLSEqbHHsnJyQldAWLKy8uxZMkSnHbaaZg8eTIuu+wyfPTRRyguLkZ2djZOP/10PPzwwx3aTkRERP3JN998gzPPPBNCCCxbtgxvvPEGdu7cCZPJBJ/PhxEjRrRrOQsWLMAnn3yCjz76CAUFBTjooIOQk5OD7OxsAEBaWhpycnKQlZWFgoICJCcnQ9d1fPnll/jzn//cYnRiWZYxZcqUeCmC2KO2trbNLKHWWK1W/O53v8Py5cvj02pqavD111/Hs4Q8Hg+mTp2KnJwcvPfee5g3bx6eeeYZPPvss+3+nGnTpsFoNMJiseCxxx5DQUEBZFnGr3/964QA1Zw5cxKCTk1f+9Of/oRFixbhkEMOabH89gTivvnmGwwfPhz33ntvwnRZlrF48eIW23LBggUttuU//vEPvPDCC3jhhRfwwAMPID8/v8VI0rHHwQcfjJtvvhkPPfQQUlJS4qNGA9HsrrPPPhuRSAQXXnhhPLtryZIl8W6NTR/FxcX7XD+iAx2DUkQDXEZGBn75y19i1apV8QBG7A/1sGHD4r8qxbrpxUQiEfzlL3/BzJkzIcsyRowYgQcffBDDhw/Hjh07EmoZteW6665DTk4Ohg4dissuuwxFRUXIy8vDpZdeipdffhn5+fk4+OCDAURrKmRnZyMnJweXX345AKCyshI33HADrrrqKpjNZhiNRixZsgSPP/44nn/+efzvf//Dn//8ZwBASkoK/vrXv+LOO+/E0qVL2719Ro4ciXHjxrX6aG9W0RdffIGLLroIy5Ytw2WXXYYPPvgAo0ePxtNPP43jjz8eL7/8MlJTUzF+/HhUVlZiw4YNSE9PxxVXXIH09HQAwHvvvYdIJAJd1yGEwPnnn4+RI0ciHA5DRAetaPFQVbXFr3cAYLPZ8Oqrr+JXv/oVNm7ciGXLluGpp57C3XffjQ8//BA5OTnxIZSJiIgOREOGDEFmZiYmTZqEq6++GlOmTMHzzz8PXddRWVnZ7qDU0KFDcfzxx2Px4sUAgLPPPhu33HJLwjzXX389rrzyyvhzWZbxzjvvtAiixHz88cctSgY0DXy017nnnosVK1bEM5/effddDBs2LJ5xXltbC6/XixkzZuDYY4/FpZdeihUrVuBXv/pVuz9j2bJl0HUda9asgaIoUFUVJ598Mo4//viE+QKBAAoKCtpczoUXXojf/e53ABC/Bqmuro7XodrbdcmVV16JVatWwW63t3jtkksuabEtb7zxxhbzzZo1C9988w3OP/98rFq1Cg0NDbjtttswfPhweDye+GPlypUAol0Wb7nlFixZsiR+3fzFF1/g17/+NSZMmICkpKR4ltzOnTtx5pln4qKLLoo//+KLLwCA2VNEYFCKaMCrra1FTU0NkpOTW7y2t0yphx9+GDt37sQ111wTn3bjjTfihBNOwLZt29p1Mbd48WL4fL54v/2JEyfi448/xpIlS3DNNddgzZo18e6CSUlJWLlyJXw+H9544w0AwLZt26BpWsLFxYQJE7Bp0ybMnj0b11xzTTyoBUTT7y+99FJMnz4dc+bM6cBW2j/33nsvnn76adx88814++23cffdd+OUU07B9ddfj5qaGqxcuRI333wzNm3aBLvdjt///vd45ZVX8O2338aXYbVa43UqHnvsMSxZsgSzZs1qNbV8+/bt2Lx5MyKRSKsZa4MGDYr/QpudnY0lS5YgJSUFp59+OgDg6aefbrMeFRERUX/X0NCAjIwMPPnkk/jss8+Qm5sLAFBVFdu3b4emacjNzY0HMUKhEFwuF7xeb6vLS09PR1lZGcrLy7Fs2bKELnlAtFzCSy+9lJAVk5KS0uaAMOeccw5ycnISHvX19R1ez1NOOSWelQUAy5cvT6illJeXh6lTp+Kyyy7DWWedhXnz5iElJQWHHXZYuz8j1uWwqZycHPz2t79NmOZyuVrN3m76+iGHHIJvv/02/gNcRUVFPOMsFArttR2tFS0HgNdff73FtvzHP/7RYj5JkjBhwgQA0ew2p9OJLVu2YPTo0UhKSoo/6uvrIUlSvFtezDvvvIMpU6bgiCOOwJtvvglFUeJZcjk5ObDZbLDb7fHnQ4YMAZCYMUY0UDEoRTSA+Xw+jBs3DkOHDsVdd92V8FogEIhnSu3cuRP33nsvZFmOX0AdfvjhuPPOO5GTk9NiuZ988kmro4u0pqysDH/+859x9tlnA4gGqiZNmoSysjLceeedOOOMMwBEay9ceOGF8ZH5AGDy5Mn43//+l3AhEggEMH36dGRmZuIvf/lLi8975plncP7557c6Mkt3iRU3HT16NO666y6oqoorr7wSaWlp8V/1TjvtNNTX12Ps2LH4+uuv8e2338aDRE09+OCDuPnmm3H11Vfjtttuixc9z83NRUFBAQoLCzF69Ggcdthh+Pnnn/faLq/Xi6FDh+K+++7DlClTsGnTJhxzzDHdsg2IiIj6inHjxsFms8FkMuHoo4/G7bffHq8XGatLlJ+fH59msViQkpKC2267LWE5sS6Ar7/+On73u9/h2WefxZAhQ+IZ3THXXnst0tPT8cADD7Srfa+//nqLH5w6MwiJyWTCtGnTsHz5ckQiEXzwwQc477zzEuZZtmwZ3n33XYwfPx4rVqzA6NGj8fLLL3f4s5p69tlnMXHixHgdT03TsHnz5oSaT7FBbWIWLVqEqqoq5OTk4KabbkJxcTF8Ph8OOuggXH755fjlL38JTdMQDoc71JZzzjmnxbacNWvWPt/n8/nw8ccf49e//nXC9LKyMmRkZLTIcDr55JOxYMECvPPOO7DZbB1qI9FAx6AU0QBmt9vxww8/4IMPPkBKSkr8V6jKykpkZGRgypQpAKK/4qxYsQILFiyIB6VOO+003HHHHQASU6pXr16NNWvWtLjoiXn66afjXQFLSkpw/PHH47TTTovP//TTT2PhwoX4z3/+g5UrV8Yv4K699lqMGTMGxx57bMIvjbH6A7E2WK1WfPTRR3jnnXdazRJSFAUvv/wyZs6c2cmt1nkVFRV49NFHcfPNN7fIJPvoo49gsVjiKd0jR45MKDRaXV2NadOm4f7778ff//53PPXUU1BVFX6/Hw0NDTj00ENxzz33oKGhAW63G5FIpEWdiuaFSy0WC1599VVkZ2fj+uuvR2FhIT788MOEeTpaGJ6IiKivW7VqFaqqqlBXV4f6+vr4Y8uWLcjIyMC8efOwa9eu+PTYD3SxmpUxI0aMQElJCZ544glceuml+L//+z/86U9/ahGwsNlsmD17Nv71r38ljCbXlrZqSnXGeeedh+XLl+OLL75AZmYmDj/88PhrP/74I+644w4cd9xxuPPOO7Fy5UqceOKJHaoptTex68pPPvkEqqpizJgx8deajmQohMBTTz2Fq6++GllZWXj00Ufxv//9D2PGjME333yDKVOm4Oijj4YkSR0O+LRVU2pfHnroIQSDQVxwwQUJ08vLy1tkSQHRAOAf//jH+CjV+yqDwOsroj32Pl47ER2wVFUFgHiNgh9//BHTp0/HIYccgsMPPxwnnngiJk6ciA8++AAZGRl49NFH4fV68dprr8VH2YvRNA2qqqK+vh6XXXYZLrjgAgwfPjz+emxkmurqarz00kuYPHkyTjjhBCxfvhy/+MUv8Pzzz+Ott96KB00+++wzPProo3j//ffjWVCapuHll1/GH/7wB1x++eX48MMP43/4gejFTezXs6Zd9pq2L6YjhUK7SkVFBU4++WSkpqbGC6QLIeKBtx9++AEPPPAA5s6di7vuugsLFy7E73//exxzzDGYOXMmjj/+eASDQXz66aewWq3YsGFDQtp/OBxGdXU1Nm3alDDN4XAgPz8fQHSfRyIRlJWVxecZMWIEbrnlFvzpT3/CsmXLMGLEiPjr9fX1CduNiIjoQBDrEtbUhg0bMGPGDBQWFmL69OkYO3YszjnnHNx9993IzMxMmLfpNdS6desAAHfeeSc0TUuoHdXUrFmz8OCDD+LFF1/c50ApH3/8cYuaTLEakx01ZcoUVFVV4a9//WuLHwwdDgcWLFgAg8GAk08+GRUVFVi7dm2rmdqdMWvWLPj9flx77bXxAWc0TcPHH3+MjIyM+Hz/+te/sGPHDtx0000Aol35nnzySdx4442YN28eNE3DqlWrcNlll3Vo5GMgWlNq0aJFCdNuvvnmVmtuxjz55JP4y1/+gkceeSShnQCwefPmVoNSzbXV3TASieDRRx/FV199BUVROpUBR3SgYVCKaIBqnjY9f/58nHLKKXj66adRX1+P//znP/jyyy/x2muvobS0FPX19QiFQjj++OMTglKapkHTNIRCIXz66aeQJAlPPPFEwrKPOuooWK1WZGZmwuFw4MknnwQAzJ49G9dddx0kSUIoFIr/anbLLbfg1FNPxVFHHRUfUjkYDCIpKQnPPfccIpFIiz74oVCozZTuYDCY8ItcT/v4449x8cUXw2Kx4LPPPoPZbMbmzZtx6qmnYseOHbjkkkvwyiuvYPDgwfjVr36FE088EZMmTcK2bduQnZ0Ns9mMDz74AA6HA4FAALm5uTCbzS2Ca88//zyef/75+PNwOIzp06fHi69qmoYff/xxrxdTTYeJBtDiYoyIiOhAoWkaVq5ciRdffBGvvPIKpk2bhkWLFsFkMuHRRx/F/fffjxdffBE33XQTbr755ngGdvNrKCBavyk/Px9OpxNPPPEEvv76awCIZ/akpaXhm2++aVGvKRwOIxwOo6SkJD7ibnV1dcIPSEA0s6aqqgqbN2+OXwe0h8FgwNlnn41//OMfmD9/fsJr+fn5ePPNN3Hvvffir3/9K0wmE0455ZR4rc/2EEKgvr4eJSUlLbKYUlNTcf3110NVVSxYsACfffYZrrnmGpx11lmYO3cuAKCurg633norLrzwwnidpTvuuAMGgwHXXXcdLrnkEowbNw533303HnroIQDRoGBrmUixbVlTUwOPxwOfzwdN01psS6/XC7/fj23btsFms2Hw4MEAgC1btuDBBx/E4sWLccstt8Rrlgoh8Mgjj2Dnzp1YtmxZu7phKooCRVHiz2N1soxGI7744gv8/PPPWLBgAQudEwGAIKIByWg0ik8//TT+PBAIiHA4vN/LjUQinXpfMBgU9fX1+/35fVFlZaWYOXOmqKqqSpj+wgsviG3btrWYf9euXeKKK64Q48ePF5s2beqydtx3331iwoQJ7Z7/pZdeEoMGDeqyzyciIuoLVq9eLaZOnSoyMjIEAHH00UeLt99+u8V8kUhEPPzww8Jms4khQ4aIoqIiIYQQ5eXlAkDCdVRT8+bNE7/4xS/E5ZdfLnw+317b8pvf/EbMmjVLTJgwQSQlJQmn09nmw+FwCLPZLIqLi/d7G3Sl8ePHi4KCAvHYY48JIYTQNE3cfPPNwuFwiJkzZwqv1xuft6GhQZxxxhmioKBAuFwusWHDBlFQUCDWrVsnhBDiqaeeEgDEv/71r/h7nnnmGTF8+HDh9/vF0qVLxejRowUA8dNPPyW0Y/HixcJsNovHH39cWK1W4XA42tyWycnJwmazibvuuksIIYTP5xOTJk0Sw4YNE0uXLm2xjn/84x9FQUGBuO2220QwGOzwNpoxY4aYOXOmEEIIXdc7/H6iA5kkBMf9JiIaCBoaGhAIBJCVldXbTSEiIupVN9xwA7Kzs3HWWWcllBxozfbt2/Hqq69izpw5vVICoD/asmUL6uvrccQRR7R4TQiBDRs2xLPGgsEgLBYLAKC4uBjvv/8+rr322vj8uq6jvr4eaWlp2LFjB+bPn48zzjgDJ598cpe2uba2FhaLJT4ITVeaNm0arFYrlixZ0uXLJurvGJQiIiIiIiIiIqIex9H3iIiIiIiIiIioxzEoRUREREREREREPY5BKSIiIiIiIiIi6nEMShERERERERERUY8z9HYDeoqu66ioqIDD4eCoGURERNQpQgh4PB4MHToUsnxg/rbHayYiIiLaX+29ZhowQamKigoMGzast5tBREREB4CdO3ciJyent5vRLXjNRERERF1lX9dMAyYo5XA4AEQ3SHJyci+3hoiIiPojt9uNYcOGxa8rDkS8ZiIiIqL91d5rpgETlIqlnycnJ/MCi4iIiPbLgdytjddMRERE1FX2dc10YBZDICIiIiIiIiKiPo1BKSIiIiIiIiIi6nEMShERERERERERUY8bMDWliIiIiIiIiGhg0DQNkUikt5txwDIajVAUZb+Xw6AUERERERERER0QhBDYvXs3GhoaerspB7yUlBQMGTJkvwaAYVCKiIiIiIiIiA4IsYBUZmYmbDbbAT1ibm8RQsDv96OqqgoAkJWV1ellMShFRERERERERP2epmnxgFRaWlpvN+eAZrVaAQBVVVXIzMzsdFc+FjonIiIiIiIion4vVkPKZrP1cksGhth23p/aXQxKEREREREREdEBg132ekZXbGcGpYiIiIiIiIiI+hhN0/DHP/4R69at67bPqKurS3geDofh9Xq77fOaY1CKiIiIiIiIiKiPURQFP//8M55++ulWXxdCQJIkpKamIi0tDZIkYc2aNfj+++8TAktCCJSWlsYLk8cEg0GMGDECb7/9dnza559/jszMTAQCge5ZqWZY6Jxa0HWB0lofPEEVDosBeWl2yDLTH4mIiIiIiIi6y5NPPolHH300YVp9fT1WrlyJFStWJEy/6qqr8Oc//xlGoxFr167Frl27cMYZZ2DChAk466yz8MMPPyQUHw+FQnjttdeQmZkZn7Zs2TJkZmaivr4e+fn5UBQFgUAAkUgEo0ePBgCoqoo5c+bg2muv7ZZ1ZlCKEhSVu/DG2jIUV3kRiugwG2WMzEzC2eNzUJjt7O3mEREREREREXXY5Yu+7dHPe/7SSR1+j9/vx7hx4/DWW28lTK+srIQkSfGA0rRp0+KZUCaTCQDw4Ycf4qSTToIkSXjzzTf3+VlCCDz44IO47rrrcNFFF+Hiiy+GoihYvHgxnn76aXz11VcAokXMu7NGF7vvUVxRuQtPfLwF68tcSLGakJduR4rVhPVl0elF5a7ebiIRERERERHRAemQQw7Bb37zG5SXl+OII47At99GA2kffPABxowZE59vxowZOPbYYxPe+/777+Pkk09GMBiEpmktlq3rOvx+f/z5P//5T6xfvx5DhgyBwWDA3XffjYkTJ+LOO+/Ehg0bMHHiREycOBFvv/02DIbuy2diphQBiHbZe2NtGep8YYzMTIpHQpMsBow0J6G4youla8sxKiuZXfmIiIiIiIiIutjvfvc7aJoGWZZx9dVX48QTT8Ttt9+O7Oxs5OXlxee74IILEt7n8XjwzTff4I033sDFF1+Mjz/+GLIsIxQKIRQKITk5GUIIhEIhlJeXo6GhATfffDMGDx4cX8bOnTtxxRVX4A9/+EN82h/+8Ae43e5uXWcGpQgAUFrrQ3GVF1lOa4vUPEmSkOW0YkuVB6W1PgzPSOqlVhIREREREREduKZPn47CwkLcc889mDRpErZu3YrKykrk5OS0+R673Y5Jkybho48+wuuvvx6f/txzz+Gtt97C8uXLE+Z//vnncdJJJyEUCsWnybKMu+++O6GmVXV1NSZPntyFa9cSu+8RAMATVBGK6LCalFZft5oUhCI6PEG1h1tGRERERERENDDceuutePXVV/HrX/8aBx98MM444wxUVlZi2LBhbb5HlmX8+c9/xv333w8AePPNN/Hdd9/FX//pp58SglU33HADnnvuuRbLuf/++1FcXBx/nH/++V24Zq1jphQBABwWA8xGGYGwhiRLy8MiENZgNspwtPIaEREREVGf8Mq53bv8C17t3uUT0YDm9/txxBFH4Ouvv8ZLL70Eo9EIAKioqEBBQUF8PlVVEQqFYLfb49PGjBmDLVu2oLa2Ftdddx0efvjh+GubNm3CVVddhRNPPBEpKSmQJAkOh6PF5z/wwAN48skn488rKiqYKUU9Iy/NjpGZSdjlCkAIkfCaEAK7XAEUZDqQl2ZvYwlERERERERE1Bk+nw8ZGRlITU1FQUEB7r//fqSnpyM9PR3PPPMM/vKXv8Sfp6enIyMjI/7eV199Fb/5zW/wm9/8Bh9++CEcDgfOO++8+Otnn302fvGLXyQEqlpz1113oaioKP6YMWNGt61vDNNeCAAgyxLOHp+D8vpAvLaU1aQgENawyxVAqt2Es8Zns8g5ERERERERURez2+3w+Xwtps+bNw/vvvsunnrqKWRnZyM1NTXhdU3TMGHCBGRmZmLGjBk4/PDDceedd0KWE3OQ7rjjDlxwwQX405/+hMzMzFbbwEwp6lWF2U7MnlKA0TlONATCKK3xoSEQxpicFMyeUoDCbGdvN5GIiIiIiIjogLdt2zZccsklWLx4MV577TU88cQTGDlyJB566CEEg8H4fJFIBCNHjsTMmTNRWlqK7OxsXHDBBfjyyy+xevXqeHDq9NNPx0033QRd1+Pv1TQN4XAYkUgEQNuZUk0Lonc1STTvq3WAcrvdcDqdcLlcSE5O7u3m9Gm6LlBa64MnqMJhMSAvzc4MKSIiIgyM64mBsI50AGNNKaIBLRgMYtu2bcjPz4fFYunt5nTYV199hZUrV2L58uX44YcfcOWVV+KBBx5AUlISAGDt2rWYPXs2SktL8fDDD+P888+HLMvYtm0b8vLyEpY1c+ZMfPPNN7jzzjvbLFh+wgkn4Oijj8azzz4Lo9EISWp53y+EgNFoRHFxcYvX9ra923s9we571IIsSxiekdTbzSAiIiIiIiIaMGw2G7766itcdtllOPPMMzFo0KCE18ePH48vvvgCCxcuhM1mgyRJLWpCx7zwwgv7/LyPPvoIAHDfffftf+M7iUEpIiIiIiIiIqJeNnbsWLz99tt7nUeSJMyaNauHWtT9WFOKiIiIiIiIiIh6HINSRERERERERETU4xiUIiIiIiIiIiKiHsegFBERERERERER9TgGpYiIiIiIiIiIqMcxKEVERERERERE1AeFQiFompYwTQiBUCjUoeUIIaDreovpdXV1Cc/D4TC8Xm/HG9pJhh77JCIiIiIiIiIialV2djYcDgcsFgtcLhfOOecclJeX4/vvv0c4HEZ5eTkOPvhg6LqOcDiMDRs2YNq0abjyyitx+umnY+XKlRBCxJc3ePBgHHrooQCA//u//8PXX3+NJUuWxF8PBoMYMWIEFi1ahDPOOAMA8Pnnn+N3v/sdamtrYbVau32dGZQiIiIiIiIiogPbK+f27Odd8GqH31JeXg4AKC0txRFHHIHf//73OOywwwAA7777Lh555BF8+umnCe+ZOXMmLrnkEixduhRTp07F+eefDwDYsmULhg8fjvvvvx/19fUwm80wm80J7122bBkyMzNRX1+P/Px8KIqCQCCASCSC0aNHAwBUVcWcOXNw7bXXdnh92oNBKSIiIiIiIiKiXhYMBvHAAw9g69atuO++++IBKQCoqKjAyJEjW7znzDPPxJFHHomsrCxYrVY88cQT2LFjB1avXo3//e9/+OCDD/C///0PRx55ZML7hBB48MEHcd111+Giiy7CxRdfDEVRsHjxYjz99NP46quvAACRSASSJHXbOjMoRUREREREfdrli75t13zXVTZ0avnjhqV06n1ERF1t/fr1+PLLL7F48WIAwOrVq3HFFVegpqYGmqbFg0WzZ8/G9OnTsXz5clxyySXx95eXl2PixIl48sknAQCKokBRlBaf889//hPr16/HkCFDYDAYcMcdd+D9999HVVUVXC4XJk6cCAC47bbbMH369G5bXxY6JyIiIiIiIiLqRZqmQZIkvPHGG7jssssQDocBRLvPpaenY/fu3fjkk0/w9ddfY+rUqfB4PKiursa9996La665Jl4M3WKxICUlZa+ftX37dtx8880YPHhwfNrOnTtxxRVXYMeOHXC5XFizZg0mTpwIt9vdbesMMChFRERERERERNSrvv/+exQWFmLUqFF44oknMHnyZEiSFK8zBQAnnXQStm7dCiCaAXXwwQdj1apVsNlskOVoeEcIsc8C5W+88QZOOukkTJ48OT5NlmXcfffdGDlyZPyxZMmS+HK7C4NSRERERERERES9aOLEidiyZQveeecdjBw5EkuXLkV+fj5yc3Pj88RGy2sqKysLN954IyRJgq7rqKmpQVpaGgBA1/VWP+uGG27Ac88912L6/fffj+Li4vgjVjS9O7GmFBERERERERFRHzJkyBAsW7YMHo8HAOByuWA2m2G32wEkBpxmzJiBq666CoFAAMXFxRg6dCgAIBwOQwjRIjglSRIcDkeLz3zggQfitaiAaHH1ptlU3YGZUkREREREREREfUhycjIKCwsRCoUAAGvWrEF+fn789WAwCABYt24dfvrpJ5xxxhnYtGkTVqxYgQkTJuCkk07CwoULoapqvD7Vvtx1110oKiqKP2bMmNH1K9YMg1JERERERERERH3QmDFj8Nhjj2HhwoU45ZRTsHTpUlx//fWYNWsWAGDu3LmYPXs2nE4nLBYLXn/9dTgcDhx99NHwer0455xz8PDDD7frsx544AEUFhbGH6+99lp3rhoABqWIiIiIiIiIiHqdy+XCjh07YDDsqbTkdDqxdOlSfP/995g9ezbeeustnHvuuQiHw1ixYgXeeecdzJ49GwBw8803Y+rUqZg9ezaOPfZYzJw5EykpKRg6dGg8s6opTdMQDocRiUQAtJ0pFcvW6g6sKUVEREREREREB7YLXu3tFuzTo48+ihdffBG33HILAOC7777D5ZdfjsGDB2PVqlUYNGgQFi9ejGeeeQa//OUv8eKLL+LPf/4z0tLSsHjxYnz00Uf47rvvAACPP/44fv/738PlcmHBggWYP38+HnrooYTPCwQC+Pnnn5Gbmwuj0YjPPvsM8+fPT5jnvffew4MPPoji4uJuWWdJCCG6Zcl9jNvthtPphMvlQnJycm83h4iIiPqhgXA9MRDWkfqfyxd92675rqu8s1PLHzcspX0z9oObWqKBLBgMYtu2bcjPz4fFYunt5uw3XdexevVqHHXUUS1e27lzJ4YNGxZ/LoRARUUFsrOzW8y7e/duhEKhhJH8usLetnd7ryeYKUVERERERERE1MfIstxqQApAQkAKiI6o11pACoiO5NdXsaYUERERERERERH1OAaliIiIiIiIiIiox/XJoNTbb7+N4cOHw2Aw4Mgjj8TGjRsBAEVFRZg0aRIGDRqEOXPmYICUw6IDkK4LlFR7sW5nA0qqvdB1HstERERERERdQdf13m7CgNAV27nP1ZTaunUrZs6ciWeeeQbHHXccrrvuOlxxxRX45JNPcPrpp+Pkk0/Gv//9b8yePRuLFi3CzJkze7vJRB1SVO7CG2vLUFzlRSiiw2yUMTIzCWePz0FhtrO3m0dERERERNQvmUwmyLKMiooKZGRkwGQyQZKk3m7WAUcIgXA4jOrqasiyDJPJ1Oll9bmg1MaNGzF37lzMmDEDAHD11VfjlFNOwXvvvRcfytBms2Hu3Lm49tprGZSifqWo3IUnPt6COl8YWU4rrE4FgbCG9WUulNcHMHtKAQNTREREREREnSDLMvLz87Fr1y5UVFT0dnMOeDabDQcddBBkufOd8PpcUGrq1KkJz3/++WeMHDkS69atw+TJk2Gz2QAAY8aMwYYNG9pcTigUQigUij93u93d02CidtJ1gTfWlqHOF8bIzKR4xD7JYsBIcxKKq7xYurYco7KSIcuM5hMREREREXWUyWTCQQcdBFVVoWlabzfngKUoCgwGw35novW5oFRT4XAYjz76KG644QaUlJQgPz8//pokSVAUBfX19Rg0aFCL986bNw/33XdfTzaXaK9Ka30orvIiy2lt8cWVJAlZTiu2VHlQWuvD8IykXmolERERERFR/yZJEoxGI4xGY283hfahTxY6j7nzzjuRlJSEq666CgaDAWazOeF1i8UCv9/f6ntvv/12uFyu+GPnzp090WSiNnmCKkIRHVaT0urrVpOCUESHJ6j2cMuIiIiIiIiIel6fzZT68MMP8cwzz2D16tUwGo1ITU1FUVFRwjwej6fNglpms7lFEIuoNzksBpiNMgJhDUmWll+9QFiD2SjD0cprRERERERERAeaPpkpVVJSggsvvBBPP/00Ro0aBQCYNGkSVq9eHZ+ntLQUoVAIqampvdVMog7JS7NjZGYSdrkCEEIkvCaEwC5XAAWZDuSl2XuphUREREREREQ9p88FpQKBAKZOnYpp06bhjDPOgNfrhdfrxa9+9Su4XC4sXrwYADB//nyccMIJUJTWu0IR9TWyLOHs8TlItZtQXOWFN6hC0wW8QRXFVV6k2k04a3w2i5wTERERERHRgNDn+gm9//772LhxIzZu3Ihnn302Pn3btm1YuHAhLrjgAsyZMweapmHlypW92FKijivMdmL2lAK8sbYMxVVeVLp1mI0yxuSk4Kzx2SjMdvZ2E4mIiIiIiIh6RJ8LSk2bNq1F16aYvLw8bNmyBWvWrMFRRx2FjIyMHm4d0f4rzHZiVFYySmt98ARVOCwG5KXZmSFFREREREREA0qfC0rtS3Z2NrKzs3u7GUT7RZYlDM9I6u1mEBEREREREfWaPldTioiIiIiIiIiIDnwMShERERERERERUY9jUIqIiIiIiIiIiHocg1JERERERERERNTjGJQiIiIiIiIiIqIex6AUERERUT/19ttvY/jw4TAYDDjyyCOxceNGAEBRUREmTZqEQYMGYc6cORBC9HJLiYiIiFpiUIqIiIioH9q6dStmzpyJ+fPno7y8HLm5ubjiiisQCoVw+umnY8KECVizZg02bNiARYsW9XZziYiIiFpgUIqIiIioH9q4cSPmzp2LGTNmYPDgwbj66quxZs0avPfee3C5XFiwYAFGjBiBuXPn4vnnn+/t5hIRERG1YOjtBhARERFRx02dOjXh+c8//4yRI0di3bp1mDx5Mmw2GwBgzJgx2LBhQ5vLCYVCCIVC8edut7t7GkxERETUDINSRERERP1cOBzGo48+ihtuuAElJSXIz8+PvyZJEhRFQX19PQYNGtTivfPmzcN9993Xk80loi50+aJvu23Zz186qduWTUQEsPseERERUb935513IikpCVdddRUMBgPMZnPC6xaLBX6/v9X33n777XC5XPHHzp07e6LJRERERMyUIiIiIurPPvzwQzzzzDNYvXo1jEYjUlNTUVRUlDCPx+OByWRq9f1ms7lFEIuIiIioJzBTioiIiKifKikpwYUXXoinn34ao0aNAgBMmjQJq1evjs9TWlqKUCiE1NTU3momERERUasYlCIiIiLqhwKBAKZOnYpp06bhjDPOgNfrhdfrxa9+9Su4XC4sXrwYADB//nyccMIJUBSll1tMRERElIjd94iIiIj6offffx8bN27Exo0b8eyzz8anb9u2DQsXLsQFF1yAOXPmQNM0rFy5shdbSkRERNQ6BqWIiIiI+qFp06ZBCNHqa3l5ediyZQvWrFmDo446ChkZGT3cOiIiIqJ9Y1CKiIiI6ACUnZ2N7Ozs3m4GERERUZtYU4qIiIiIiIiIiHocM6WIiAYwXRcorfXBE1ThsBiQl2aHLEu93SwiIiIiIhoAGJQiIhqgispdeGNtGYqrvAhFdJiNMkZmJuHs8TkozHb2dvOIiIiIiOgAx6AUEdEAVFTuwhMfb0GdL4wspxVWp4JAWMP6MhfK6wOYPaWAgSkiIiIiIupWrClFRDTA6LrAG2vLUOcLY2RmEpIsBiiyhCSLASMzk1DnC2Pp2nLoeuujehEREREREXUFBqWIiAaY0lofiqu8yHJaIUmJ9aMkSUKW04otVR6U1vp6qYVERERERDQQMChFRDTAeIIqQhEdVpPS6utWk4JQRIcnqPZwy4iIiIiIaCBhUIqIaIBxWAwwG2UEwlqrrwfCGsxGGQ4Lyw4SEREREVH3YVCKiGiAyUuzY2RmEna5AhAisW6UEAK7XAEUZDqQl2bvpRYSEREREdFAwKAUEdEAI8sSzh6fg1S7CcVVXniDKjRdwBtUUVzlRardhLPGZ0OWpX0vjIiIiIiIqJMYlCIiGoAKs52YPaUAo3OcaAiEUVrjQ0MgjDE5KZg9pQCF2c7ebiIRERERER3gWDCEiGiAKsx2YlRWMkprffAEVTgsBuSl2ZkhRUREREREPYJBKSKiAUyWJQzPSOrtZhARERER0QDE7ntERERERERERNTjGJQiIiIiIiIiIqIex6AUERERERERERH1OAaliIiIiIiIiIioxzEoRUREREREREREPY5BKSIiIiIiIiIi6nEMShERERERERERUY9jUIqIiIiIiIiIiHocg1JERERERERERNTjGJQiIiIiIiIiIqIex6AUERERERERERH1OAaliIiIiIiIiIioxzEoRUREREREREREPY5BKSIiIiIiIiIi6nEMShERERERERERUY9jUIqIiIiIiIiIiHocg1JERERERERERNTjDL3dACIiIto7XRcorfXBE1ThsBiQl2aHLEu93SwiIiIiov3CoBQREVEfVlTuwhtry1Bc5UUoosNslDEyMwlnj89BYbazt5tHRERERNRpDEoRERH1UUXlLjzx8RbU+cLIclphdSoIhDWsL3OhvD6A2VMKGJgiIiIion6LNaWIiIj6IF0XeGNtGep8YYzMTEKSxQBFlpBkMWBkZhLqfGEsXVsOXRe93VQiIiIiok7ps0Gp2tpa5Ofno7S0ND6tqKgIkyZNwqBBgzBnzhwIwQtxIiI6MJXW+lBc5UWW0wpJSqwfJUkSspxWbKnyoLTW10stJCIiIiLaP30yKFVTU4OpU6cmBKRCoRBOP/10TJgwAWvWrMGGDRuwaNGiXmsjERFRd/IEVYQiOqwmpdXXrSYFoYgOT1Dt4ZYREREREXWNPhmUOu+883DeeeclTHvvvffgcrmwYMECjBgxAnPnzsXzzz/fSy0kIiLqXg6LAWajjEBYa/X1QFiD2SjDYWF5SCIiIiLqn/pkUGrhwoW4/vrrE6atW7cOkydPhs1mAwCMGTMGGzZs6I3mERERdbu8NDtGZiZhlyvQoru6EAK7XAEUZDqQl2bvpRYSEREREe2fPvnz6vDhw1tMc7vdyM/Pjz+XJAmKoqC+vh6DBg1qMX8oFEIoFEp4PxERUX8hyxLOHp+D8vpAvLaU1RQdfW+XK4BUuwlnjc+GLEv7XhgRERERUR/UJzOlWmMwGGA2mxOmWSwW+P3+VuefN28enE5n/DFs2LCeaCYREVGXKcx2YvaUAozOcaIhEEZpjQ8NgTDG5KRg9pQCFGY7e7uJRERERESd1iczpVqTmpqKoqKihGkejwcmk6nV+W+//XbceOON8edut5uBKSIi6ncKs50YlZWM0lofPEEVDosBeWl2ZkgRERERUb/Xb4JSkyZNwnPPPRd/XlpailAohNTU1FbnN5vNLTKriIiI+iNZljA8I6m3m0FERERE1KX6Tfe9Y489Fi6XC4sXLwYAzJ8/HyeccAIUpfWhsomIiIiIiIiIqO/qN5lSBoMBCxcuxAUXXIA5c+ZA0zSsXLmyt5tFRERERETUNV45t8Nvua6yoV3z/W3wgx1eNhFRd+vTQanmQ2BPmzYNW7ZswZo1a3DUUUchIyOjl1p24NB1wTolRERERERERNTj+nRQqjXZ2dnIzs7u7WYcEIrKXXhjbRmKq7wIRXSYjTJGZibh7PE5HNGJiIiIiIiIiLpVvwtKUdcoKnfhiY+3oM4XRpbTCqtTQSCsYX2ZC+X1AQ41TkRERERERETdqt8UOqeuo+sCb6wtQ50vjJGZSUiyGKDIEpIsBozMTEKdL4yla8uh62LfCyMiIiIiIiIi6gQGpQag0lofiqu8yHJaIUmJ9aMkSUKW04otVR6U1vp6qYVEREREREREdKBj971+an8KlHuCKkIRHVan0urrVpOCSrcOT1DtyiYTEREREREREcUxKNUP7W+BcofFALNRRiCsIcnS8hAIhDWYjTIcrbxG1J04GiQREREREdHAwahDP9MVBcrz0uwYmZmE9WUujDQnJXThE0JglyuAMTkpyEuzd/fqEMVxNEgiIiIiIqKBhTWl+pGuKlAuyxLOHp+DVLsJxVVeeIMqNF3AG1RRXOVFqt2Es8ZnM0OFekws2Lq+zIUUqwl56XakWE1YXxadXlTu6u0mEhERERERURdjUKof6coC5YXZTsyeUoDROU40BMIorfGhIRDGmJyUdmVbEXUVjgZJREREREQ0MLH7Xj/S1QXKC7OdGJWVzBo+1Ks6EmwdnpHUS60kIiIiIiKirsagVD/SHQXKZVnijT71Ko4GSURERERENDCx+14/EitQvssVgBCJXZliBcpHZiRBFwLrdjagpNrLLk/U5zUNtraGo0ESEREREREdmHiX14/ECpSX1wfi3Z2spujoe7tcARhkCbW+MO57ZwNHL6N+g6NBEhERERERDUzMlOpn2ipQnp1iBSSgoiHA0cuoX+FokERERERERAMTM6X6oeYFyu1mBf9avQPlDQGMzNyTaZJkMWCkOQnFVV4sXVuOUVnJvLGnPikWbH1jbRmKq7yodEcz/cbkpOCs8dnM9CMiIiIiIjoAMSjVTzUtUF5S7UVxNUcvo/6No0ESEdEB45Vzu3f5F7zavcsfgH7Y2dCu+f626NsOL/v5Syd1+D19xeWdWN+O6M/bpkN4TiBqE4NSBwCOXkYHCo4GSURERERENHAwKHUAaDp6WVIrI5T1pdHLdF0wE+YAxX1LREREREREHdH7UQrab/1l9LKicle8ZhBHBzywcN8SERERERFRRzEodQCIjV5WXh9AcVW0tpTVpCAQ1rDLFegTo5cVlUdHAazzhaPtc0bbt77MhfL6AGZPKWDwop8aiPuWWWFERERERET7j0GpA0RfHr1M1wXeWFuGOl+YowMeYAbivmVWGBERERERUddgUOoA0ldHLyut9cUzuDg6YN/XkSygve5bROudrd1Rjy+2VONXBRm9fizur4GYFUZERERERNRdGJQ6wPTF0cs4OmD/Ec8CqvTCFYhAliWMSLfj0qPzMDonpcX8be3bel8YJTVeuPwR+MIa/vrRZny2ubpfZxMNxKwwIiIiIiKi7sSg1AGmL9a66crRAfvi+h0oYllAZfV++MMaAmENEU1ga7UX326vw00nHYwzxmUnvKe1fVvvC6Oo3IWgqsGkyLCbFQyymft9NhEz/oiIiIiIiLoWg1IHkL5a66arRgfsq+t3IIhlAZXV++HyRxDSdNhMBtjNEiKajjpvGI99sBn56XaMaZIx1WLfAiip8SKoaki2GOAOqki1GWE1yTDIRlQ0BPDGd2X9MpuIGX9ERERERERdS+7tBlDXiGW5rC9zIcVqQl66HSlWE9aXRacXlbt6rW2x0QFT7SYUV3nhDarQdAFvUEVxlbddowP25fU7EJTW+lBc6YU/rCGk6XBajTAqMiRJgsmgIDXJhAZ/GC9+uR26LuLva75vd7uDcPkjMCky3EEVkiQhqAqs3d6AH3a6sNsdxPsbduPDDbt7cW07p2lWWGs6kvFHREREREREDEodEJrXukmyGKDIUrTWTWYS6nxhLF1bnhBM6Gmx0QFH5zjREAijtMaHhkAYY3JS9tmdqz+sX3/nCapwBSIIhDXYTAZEy5TvocgyDLKMrdXR7mlNNd239Y01pDQhYDcpkAD4wypMhmiwxmpU4A6o+Oeq0n4XSIxlhe1yBSBE4rEWy/gryHTsM+OvK+m6QEm1F+t2NqCk2svvABERERER9Sv8Sf8A0F9q3XR2dMD+sn7dpSfqaDksBsiyhIgmYDe3XLamCxgVCQ3+CN76vhwTcgfh6BHpMBiice3Yvv1iSzX++tFmpFhNqGgIwBfWkGwxxvebJEmwmxT4wmq/Kwoeyworrw/Ej0erKTr63i5XoF0Zf12J3VmJKKa2thYTJ07Ep59+iry8PADAddddhyeffDI+z4gRI1BcXNxLLSQiIiJqHYNSB4D+VOumM6MD9qf162o9FXjIS7NjRLodW6u9iGg6TIY921oIgXp/GMGIBt0r8NwX27B49XYMdVow67gR8eLnsizhVwUZ+GxzNb7dVgd3MAKbydAkkCjgD6tIs5uRn2rvl4HEWFZYbJ9UuqP7ZExOCs4an91jwaBYd9Y6XzgaHHNGg2P9vZg8dQ8OEHFgq6mpwemnn47S0tKE6d999x3++9//4qijjgIAKErrf0OJiIiIehODUgeArhzdri/q7fXrrRu6ngw8yLKES4/Ow7fb61DnDSM1yQRFlqHp0YCUL6hCALCZFGQkmRDWBHbU+vHg8g0AkBCYOnt8DjZUuFFa68cgRYYQAqoeDUhZDAry0+2wmg2o9IT6ZSCxsxl/XaV5d9ZY0C/JYsBIcxKKq7z9LguNug8z6g585513Hs477zysXr06Pk1VVRQVFeHYY49FUlL/CfwTERHRwMOaUgeAvljrpiv15voVlbvwwH834J5lP+Ev/92Ie5b9hAf+u6Hb6yH1Rh2t0TkpuOmkgxuLmkeio/BFNAQjGgQAq0nGIJsJiqLAajIgM9mMQFjDwpUlUFU9vpzCbCcuOzofyVYDAhENnqCKsKojzW5GYbYTg+ymfh8ojWX8jR2WguEZST0a/OlId1Ya2DhAxMCwcOFCXH/99QnTfvzxRwghMG7cOFitVpxyyinYsWNHL7WQiIiIqG0MSh0AumJ0u76st9av+Q1dbpoNBlnG6q21mPfuRvxY1tCln9dUbwUezhiXjScvGI8TRg3GQWnRmkm6LmAzKUi1mWE27un+IUkykq1GlLsCWLW1JmE5J44ajJNHDcEQpwWHD3NiYt4gjD8oBYPspgMiUNqb4t1ZTW13Zw1FDszurNR+HCBi4Bg+fHiLaRs3bsRhhx2GJUuWYMOGDTAajZg1a1abywiFQnC73QkPIiIiop7QP9MUqIW+Uuumu/T0+jW/oav3R/BzpRvugApN11HeEMBtb6zHQ2ePxuiclC79bKB362iNyUnBo9PHorTWh7e+L8dzX2xDRpKp1XokJoMMd1BFtSeUMF2WJZw9IQflDYHG7ocGaAIIhNReKQp+IOnt7qzUPwz0ASIGugsvvBAXXnhh/PmTTz6J4cOHw+12Izk5ucX88+bNw3333deTTSSiXnBd5Z3duvy/DX6wW5ffUZcv+rZbl//8pZO6dflEAwXvWg4gvV3rRlV1rNpag2pPCBkOc8LobF2hJ9ev6Q1dvT+ConIXQqoGm8kAg6zAGNGws96P+Ss24fZTD+3yoFhvBx5i3dMm5A7C4tXbEdYErK3Ex8KqDoMiIcNhbvHagR4o7YiurEsW6866vsyFkeakhIBDLAttTE5Kj2ehsZh23zKQB4igllJSUqDrOnbt2tVqUOr222/HjTfeGH/udrsxbNiwnmwiERERDVAMSh1gOjO6XVd4+4dy/GPlVlS4glA1AYMitRidrSv01PrFbugsyTJ+rnQjpGpIthjjAQCLUUFY1VHnDXdLUem+Eng4ekQ6hjot2FHrh8UoQ5L2BBmF0OEORHBQmg1ZKRas29nQIhjR24HSvqCrC03HurOW1wfigVOrKVoEv7ey0FhMu+/p7cA29a4bb7wRkydPxowZMwAA3377LWRZbjPQZDabYTa3/HGBiIiIqLvxapT229s/lOPB5RsQCGtIthphtsoIqXqro7P1F7EbuhpvGO6ACpvJkBAYUnUBgyJjiNPSLV1g+krgwWCQMeu4EXhw+QZUuUNIthphMsgIq9GAlNEgIyPJjAeWb2wzGNE8kKjrAiXV3gERpOquERT7UhZai3VMllHtDWH11lpsrvTgtlMO6ZYurrR3fSWwTb1j3LhxuOOOOzBkyBCoqorrrrsOl156KWw2W283jYiIiCgBg1K0X1RVxz9WbkUgrCEz2RzPpLGaZFiMMqrcISxcWYLfFmZ1aVe+7ha7oVu9tRaarsMgN+0CI+APq0izm5GRZEZprb9busD0lcBDLKAYy4RzB1UYFAmZyRbYTQp8Ya3dAZeBlFHTvC5ZLCiQZDFgpDkJxVXeTmfZxYrP/27sULgDESRbjXBajT0e4Gu+jg1Naq+pmo6yhgBufeNHzD97DMYwMNWj+kpgm3rHJZdcgo0bN+KMM86Aw+HAmWeeiblz5/Z2s4iIiIhaYFCK9suqrTWocAWRbDUmdO0CWo7OdtzBmb3Uyo6L3dBt3u1BeUMAxogGi1GBqkcDUhaDgvx0OwKNgZX2dIHpaM2dvhJ4AKKBqd8WZsVrhqU7TPh0UxV+qvC0O+DSXVlDfVV3FZreW2Cvp4+LpuvY0Fh7LdhYe81mUhCMaCirD+Ch9zbh9tO6vvYa7V1fCWxTzxAicSTFefPmYd68eb3UGiIiIqL2YVCK9ku1JwRVEzBbW8+Camt0tv6gMNuJW089BLe98SN21gcai3rLSLObkZ9uR4rNiOIqb7u6wHQ0Q6i7Aw+dKUptMMjxwGJJtRclNf4WARchBDxBFVajgnVlDSip8WJkpqNbs4b6qu4oNN3XAnvxdWysvRZUNTitRgDRfWg2KohoOup83VN7jfaNdd2IiIiIqC9jUIr2S4bDDIMiIaTqsJpaBqb2NjpbfzAmJwUPnT0G81dsQp03jCFOCzKSzAhEdBRXedvVBaajgYTuDjx0RRe61gIudb4wttV4o123dB1hVcf/fbQFs44bAZtJ6ZXh6XtzRLiuLjTdFwN7sXWs9obitddiASkA0HQBRZYxpJv2L7VPbw2AQURERES0LwxK0X5pz+hsuWl2HD0ivRdbuX9G56Tg9lMPjQdySmv97e4C09FAQqvzCwEBgVSbERWuAN74rqzNwMO+gjDNA16W5Ggx99Vba7F5twe3nnpIu2r/NA+41PnCKCp3IdTYdcsoop+5rdaHJz7eglMLh/T48PSdCb51ZRCrqwtNd1d3wP3RtPaaqumwmfbsXyFitddMSE8yYXs31V7bX70ZuCQiIiIiGugYlKK4znbp2tvobFaTgquOG96vipy3prNdYDoaSGg+f70vjJLG7CNNjwan3t+wG5OHp+HkwiEJy9tXEKZ5wKu+SVFqTddR3hDAbW+sx0Nnj97naGlNAy4jTHZsq/EipGpIthghSYArEC0EX5iVjOJqH/5XXAOTQerSrKGOBN86UoT9510uVDSEoAsdOak23HzywRh/UGq72tVUewtNA2jXaITd0R1wf8Vrr1V6UNYQQDAS3Y+hiA5/4z7NS7Mj2IHaaz1pIBXeJyIiIiLqi/rWHQL1mv25OWtrdLbcNDuuOm54/PX+rjNdYDoaSGg6f31j9lGscLRBlhDRdNT7I/jnqm3IHmSN75v2BGGadqGrbyxKHYovW4ExomFnvR/zV2zC7afuvSh104DLTxVu1PnCsLZSCF6SZWQ5rahyh5CZHM2o29+soY4G3zpShL2owhWvkyYA7KgP4txnvsL5Rx6E+88Y3a593tS+Ck0DwAP/3dCu711XdwfsKoXZTtx2yiG49Y0fsb3WD90voOmAIgOKLGFbjQ8GRcLk4entzgrrCX2tPhcRERER0UDEoBS1enPmD6n4dlsdNlS4cdnR+Thx1OC9ZgU1H50tw2HG0SPS+32GVGc0zeJp8Ic7lCEUDzyEVJTUeJsVjhbQdAGzQUadLxTvxgegXUGY08dmIRTRYWksSr0nsyk6v8WoIKzqqPO2ryh1LODyzMqt2FHnBwQSCsEPspsA7Am8HTMyHSuCu/drePqOBt8SstOEgCcYASDw5dYafL6lCscWZMa3X1GFC7tdQegCUJq8LaIDL6/egUyHGX/8zS/22r62tlNrWXYbdrk7FBTp6u6AXWl0TgquPHY47n9nA4JhDSnW6Oh7YU2goiEAq0nB2GHOPtMtri/W5yIiIiIiGogYlBrgWrs521OwOoLSWj/uW/4TVpfU4uwJe6/Fs6PejxSbCcNSbV1al6U/1XxpkcVjkFHnC6POF8aYnJR9BhJigYdvS+vg8kfihaNDqgZ3IIxAOFo4Hk268RUMTmpXF0F3IB1mY7SGVKwoddP5I5oOALCZEkfO25vCbCf+dEIBdruCsBplOG0mOMwGoMlyY4G3ccNS8IvBjk4PT9/eQEIs+NY0O63eF8bGXW5Ue0MIqxqEAG5+/UccNSINvx2dhZ93RTOkYgGphONLF9AE8OwX23DVMSNgMrWe9bY3zbPs2loXu1lBZrIZ26p9eGFVKR46a3Q8sNve7oC98d3QdYEfy1zIcloQ0XR4ghp8YR2KLGFoihUGRcK6nS6cPmZoj7evtfNHX6zP1Z5299XzHhERERFRZzEoNcA1vzlrXrB6kCIjENHw7fY6lDfsvRZPd9Rl6U81X9rK4qn1hVDtCeHHsgaMyHDsNZAQCzxsqHCjNOyHySAjGFFR5wsjogkYFQmpdhN0XaDeF8bfPyvG73+Z264ugslWY7wotabrMMh75g9FVNT4wpAlCdvr/Akj5+1rOw9PT8LoHCfWl7mQ3Swg1TzwJstSp4enb28gIRZ8i2Wn1fvCWLujHg3+CCABRkWGLoCIquPzzTUoqfah1hvtstciINW4T3RNwBtU8eYP5Tj3iIP22dbOrEvT0QvDqo5d7krc+obAzGPy4/tgX90Be+s7EVufERkOJJkVeEIqIqoOo0GGw2yAN6TFgzyxoFBPBFvaOn+MyXZ2SX0uVdW7JTu0P533iIiIiIj2B4NSA1zTGkZCiGYFqyUIIRCM6BiabEGdr2W3ru6sy9Kfar60mcVjjtZWCkR0hFUd9b4QKt2i1UBCLDNC0wV+O3oIdtb5EYho8IVUqLqA3azAbFDgDakIRnTousDWKi+e/mwrksyGfXYRdFqN0aLUuz0obwjAGNFgMSrwhzXU+UIAgBS7EUYlelMdGzmvPdt5cn4qNlS4UVThQn6qHdbG9rQVeOtM9kl763PFgm/ry1wYabJHC8UHI5AkwKRIiGgCFqOCVJsRnpCKXa5ogW7RxucKEes8CZQ3BDrc7r2tiyVZhjsQQa0vjJJqLzRdh91shNUowxVQ4138mu6Dzhbd704J+0aS4LAYE16P7Zt1Oxvw0urtPRJs2dv5Y3OlBxFN36/6XG//UB6vo6dqAgZFwlCnBbOOG7FfdfT603mPiIiIiGh/MSg1wDUtnqwL0aJbl6oLKLIEk1FBltOQ0KWlO+uy9HTNl/3tKtNa5kvTkfPCqg5dCORn2HFWYRbGDUtJ+IzWuv05rdEME10XMBsVyBLQ4I9A1XQISYLVpMBuUlDlCcEXVhHW9L12ETxokA076v04a3w2druDqHQHEYpoCER0yLKENLsJFqMCVyCSMHLe3rZz03Z7gyrqA9GuioNsJqTaTV2awdOeQt8mgwR3IIIx2U5srvSgqMKNak8IQhdQFBkRTUCWJTjMBsiyDJvJAH9YhSTJAPRWP1cIAUmKZmNlp1j3ez1i6xLRdKwprUMgosEViEDVBaxGBbouAFmCySBjeIYdle5Qi33Q3sBeT3UB29e+8QUj2OUKYMGHmyFLEgqHJsPuNHZbsKU954+QqqGiwY+CwY4O1+d6+4dyPLh8AwJhDclWI8xWGSFVx45aPx5cvgEAOhWYYq0rIiIiIhpoGJQa4JoWTx5kM0LTBQzxm53oSGppdjMcZgM0gYQuLW11pxJCwBNUYTW2vzZRcz1Z86UzXWWa3+y7ApGELJ7mI+dZjDLcARVldQGsKNqNXwx27DPbrNYXimdF2c0GuAJhRDQdsiRBkSUkW4wwGmSouoDFoES7SFV6MDTF1qKL4NhhTvzlvY3xdXRYjPCHNShSNBSTZI4GIl2BSIuR89razs3bneW0wh9SUVrrg91swEVH5u6zQH57xLa1KxBBhsOM7TW+VgMJW6s9ACQ898U2hFQdEU2HL6QiENGgCUAW0eCew2yA2RjdT0pj29KTTAiEVWgCgB4NXAkRXa5ANEtqkNWAM7toJElfSEWtL4Q6bxgOqwFCRLsVhlUddf4wTIqEwclWJFuMkCW5U8d6d3cBa/odsJsVjMiwo6jc3aII+0/lDfipwh3dtohmne1yB3DIkGQclpWMwclmlFT78MKqbXjorDFd0v2tPeePsno/LEZlr/W5AKCk2psQ1NN1gX+s3IpAWENmsrkxoAlYTTLMBhmV7iAe/2gLDs1yYGSGY7+D283b3du1roiIiIiIuhKDUgNc0+LJFQ0BCAhENB2SJMEfVuMBCkgSAiE1oUtLa92pmtbFUXW9Q7WJmmpvV6191XzZl850lWntZj/DYd7THcistBg5L6LpMBlk5GfYUdUk8wVoe+S8MTkpCKt12OUKwhOMIBDRoUhSQmAlFqTKTLag3hdGapIJ9f7ELoJjhznx3x93tVjHsKqh1hdGWNURkLS9jpzXfDvvLaPjoDQbtlX78MGGSkw5JHO/glLNt3VE01HrCyEQ0RLqc22t9sTr+qTYTPHpYU2DUZZhkAUcFiOsRiXhZl/To5GStCQzxg5z4p11u6AJQNcEJETLYwkARlnC5cfkd6rIeXO6LrD0+3IkmQ3QNAFfONp1UJEkSDIQUnVIkJGXZgMaM+I6eqx3dxew1r4Dg2wmGGQpIcjzY1k9ft7thUA0GBU7FMKqwPpyFyrqAzA0BuN2uatwy9IfcdnR+fsdNGvP+cOoyDh7Qg7Wl7tarc8FAA/8d0OLoF5+mg0VriCSrcZ4QAoAQqoGTzCa8bajzocbX12HSfmpHQoC9tR5b3+xCDsR7ZdXzu3U266rbOjadnTSdZV3dvxNr6R0eTuIiA4UDEpRvHjyf77biQ82VKLeH4HdpCQEKFrr0tK8y07zIulGEb1J6Uhtopj2dNXaV82XfelMV5m2bva31/hQ6wshpGrIT7fHu0EC0bpc0YwzEyQAVqOMHxszyGRJajszAsCwVCsaAhEosgShCzhsJpgUOV7vyx0IA5BQWuuDL6TBZlIwIiMJxxSkY+ywaJe9v7y3sc2gV1G5C0IAvxichJS9jJzXfDt3tlB38+1fUuPF5spohtMvBidheHrSPrd1SNXgDakoq/fDqMgwGSQAEjIc5oTui0kWA0YPdaLaE4YnGEEoosLWJKgU2y+yJGFMthN3TR2FgswkPPvFNniDajSQIkkYZDXg8mPyce2vCzp3oDXTtCh4RNOxqdINf4OGcGOA0WpUYDLIMDTW9urosd7dXcDa2i8VDYFoXaUUC+r9YexyqSiu8gIAzIoEVReQJSl6YDeOaFjrCyMnxQKL1QB3QMXGipY1tDqjveePccNSMG1cdosAS6yWV2tBvW+21SGs6hhk3VM3K6RqqPOFoTdmmuoaYJClDgcBe+K8t79YhJ2IiIiIuhKDUgRgT/HkXw5Pwz9XlcIXVuMFq71BtdWC1U27/o0w2ZsVSQdcARWpNhMOGmTFthp/iyHu96bpspt3B2pPzZf26GhXmb3d7BcMdiAQ0eANaSip9iGs6rAYZUQ0PR74CEQ0fLe9AaqmI6zpePyjLfjNIZmtZkbE6lHVekPwhDRIADRdhzGswmg1QdV0uANhBFUdFoMCRZJgNykYZDdhe50fnqLdKBjswI56/17XMS/Njjp/GA2BCHIG2fY6cl5TzTM6mgck91aoG4je2C78vARrttfB25j1kWQxYGLuIFx17AiMykrea+H4kmofMhxmzDpuOLxBFc99sQ0pNlPLdZRlHDLEgXVlDYho0XYmmaOnPV9IhS6AQ4Yk4ewJOZBlCX/8zS9w1TEj8OYP5ShvCCA7xYozx2W3yJDan0yRptsuSTZgcl4avt5Wi3p/BMlWAxRZgjeoIaLpnTrWu6ILmKrq+Ly4Cl9urQUg4agRqTh2ZDTrbV8BrzS7GbOnFOCttWX4fmcDzAYJiqxAFVo8YypWVF4ACKo6rLLcaiZhZ7Nv2nP+GJ3thC6iGVsOiwGjs53RkRb3MmjB4GQzfixzQdV0hCIarGYZgIAnGIGuCxgNMjRdQJaAFLsJQ5ItHQoC9sR5b3+wCDsRERERdTUGpShOliWcXJiF7EG2PUPOe0JtDjnftOvfTxVu1PnCsBoVqHpjBgokhFQda3e42pU507wtsWXvrebL/nQZ6WhXmX3d7I/IcKCs3o8Mhwm73EG4AypMBhl2U3QkOn84GrAxyIAmBDZUuFFRH4AOJGRG1PvCWLujHq5ABBFNhy6AWBnuWl8EQVWHvTELy2JQkGo3wh2M1v7KsJvgDalYX96Aee9uwPVTChAMa1DNOmq9IRiVaJZFrP02swGDrCbYTYYObeemGR12s9Ji1MZYd8XWCnUXlbvw4H83YNMuDyQJjV0co0GizzfXoModwsyj8/dZOH6XO4gkiwHHjExHSNVhbaNr3RCnFbW+MFLtJmyt9sIViACIBlIm5abiymOHJxyPJpOCc484qM3jZn8zRZpnw0iyhIOHJKOo3AV/WINJkSHLQEQTKK7yxveBrgt8saU63k3x6BHprQZ493ZcCyEQ0aJZTSt+2o2TRg1OyE4DokW8H1qxCbtdQTT2bsQ//7cNWU4LLj0qb58Br+LqaAagLMsQAjDIMiQ52j1R03VEswf3vC+i6RCNmYTJFiOUTtbQampf5w+DIqHWF8J9yzbAFYhAliWMSLfj0qPzYDcb9nrshSLR0TCrvGEMliQoioywKqKZbUIgrOpIshgwxGFuM7jdVkCzJ857ncUi7ERERETUHfpdUKqoqAgzZ85EcXExrrjiCjz88MMtbo5o/3RkyPlY179nVm7Fjjo/IACD0hiIiWjwhdV2Zc601Y7ZUwr2BMia1XzZ31/kE4IDZgWekIqIqsNokOEwG1p0ldlrEEsIqLqOkKZj6tihsJsN2LTLg/x0G7ZUeeELCzitRgQj0TpOEoB6fxi7XQEYDTJCEQ1jh6VAArBxVzTAF6t3ZJABU2PdHVUAwbCG3FQbanxhKJIEdzBa+0sXAm+tq0BEixbn3uUKYe2Ob2GQJZhrFUhoLJBuNSA/PQmpdhMCYQ2pSSZcdORBWL2trt3buWlGR2ayudmojXsK5Dcv1J2XZscb35WhpNoHRQZSbCZEc2eihb7dwQhKanx4Z11FuwrHb6xwo9IV3FPPq40uT6l2E+45fRQAtNldsD26IlOktWyYQXYTCrOd2FrtwW53CDajgoimxffB1movbvnPOlS4glA1Ee0m57Rg1nEjWozy1lYXsDpfGBt3uVDlCSGi6Xjq0614efV2TMxNxVWNgbm3fyjHnW8VwRNUIQFQGmtq6QIobwjirx9tQZbTgixn66MQNg3kZqdYIUsSVCFgggSjIkEXEvTG4vExqiZgtyjIS49ui66qm9TW+SM7xYpKdxDFVV74wxr8IRUhVWBzpQdfltTgvEnD9nHsmRDWBIKqht3uEBwWA/TGYFtEi3bfGzUkGZIcK4C+Z33aE9Ds7vNeZ7EIOxERERF1h34VlAqFQjj99NNx8skn49///jdmz56NRYsWYebMmb3dtANO8yHnVVVvM0ujMNuJP51QgN2uIKxGGclWI7ZUeuELq+3KnNmbjgTIOioWHPi6pBYRTYcnqEHTBRRZgsMSLYQ8eXh6mzW0YmJZFHWNRcNf/HI7slOscFqN2FEfQL0/AqtRgS+koc4Xgi4ETIqMYCT6ecGgipKIDyFVR26aFVWeYDwgpcgSzAYZiizBoMjxEeIqGoIIazqSzAak2c3QhcDWai90EQ0kAIAmgEBEBwBEdIGhTgtUPRqc8IVcKByajFpfGGNyUnDiqCE4cdSQdm/nphkd2xq7K1qbdFdsWiC/6U15aa0P68td0IWA3RwtAh8jSRJsJgP8YRXban2wGpV9FI6XkJFsRpUrCFUX2OXyY6S55ah8sS5PsQBUR0eCjOmqTJG2smGMigyn1YTMZAumj8/B2GHRblrv/FiBB5dvQCCsIdlqhNkqwxdWsa3Gh/uW/QRdCJx5eE58+a0Fvep8YazdXg9XIAxNAFaDgkE2I7whFZ9vrkaVJ4jbTzkEz3xWDG8sICUjvo6yQGMGpIbdriD8IRWOJjWVYgJhDSaDhAZ/GCMy7LCbFXiCKgyyFD+WQ6oOvTFVSgKQ5TRjeGYyUhuL63dl3aTm5w+7WcHLq7djS5UHLn8EvrAKVY92jdUEUOUO4R+fb0NOihWBsKXNY89hMaIgOQk/V3rgD6vRLnuNx8KoIcnITd/TvS62Prtdwfjx01ZAM9ZWTRe48Mhotp4vpPWJYuL9pQg7EREREfUv/Soo9d5778HlcmHBggWw2WyYO3curr322o4FpXw+QGnlolpRAIslcb62yDJgtXZuXr8fCX1XmpIkwGbr3LyBAKDrbbfDbu/cvMEgln+/E89/UYJd7hA0TUBRJGQlm3H5r4Zj6i+jxZ+HpydhXKYFG3fWwyiZEXZ7kGqQYQxrAARCwQicKc545sy28lps31GF/LZ+UbfZoIvor/Nelw8OAzA6dlMW8CfOa7VGtzMAhMNAJNL2ujWZV1YjmJBuwkffNiAY1uGwGmAyyAhHdDS4Vcg2C8YOi9aZQTiMPAtwaLKMn8rrkJJhhyRJaPBHUFzhgkvI0CUFg5MtyLYpqK2sg1mRkKTIqPb7oSsSImEdVqFDNZigiWghZLOkQahhSBrgqQ1jV8gP2R+AtfF1WMzxIcsUXYNTjyCk6ki3ATaLAUOcRqTbjFi+fhdkKJAN0e5gkqrCFIlAlqJZLnIYCCMCm9kAuwTUBiL4fqeOsTlOnDV2COTGbTrcJgG2xmBDbDsbjYApGjCApgHBIACgMMWA6385FC99tR2f1TQgFAZkkwlpDhvy0+1ItRpgCPhQ5wlB8kfgrqkHrEZoXi8soSAsBis0JbpcSddhjoRggoAejsAY0JGblITdVXUwOMwIePywWSwAJAhdR9jlASRg+/YAIppARBfQvAo0jxd5g1NgtFujXZ4a/Bhi1HH2ISnxdUzQge/99rpAQqaIMZi4vFwzsGNnFbbvSEP+4OS9niNi2+7tH8pRXONGpWSKZ8OcfeggHNY4MqPq8WLRB0WAN4CDkk0IqSHUhBWEVQFdFwi6PXhk6VqMtAGjcwZFj2sA0w8ZhOrd9Siu8mJIsgVbqzwIeTwwawImRcIgqwlyJAgzdHjDGirKQ/j7Z8XYXhtdJ6sagqFZ/EM0BqZkFfi50oMJuYMgSRIM4RAkXYMQAmXlLgASXvpoA0KqjiEGFR5EAytmowyrFoFJVRFUo+e1ggw7xg6xQpJUIKgibLLsqZuUZNj7udXWpAZaKASorQdDZADD02yALKOk2ovSigboXi80fwSKJiDpAlZZgiRFu9U2hA0oawAMioSRKSaEXY3nspAG0Xguy7SZMHqQHTnJaSipD8JqVOD3+jEhKwmyIgGNx4YQAnXVPhw2NBmrt1TGA5qKpkIJB2ABkJIso6TahRc/+AmD7EaU1PjgFQqMFlM0i2r0YAy3GVue92LMZsDQ+KdcVaPboi0mU/T73NF5NQ3JWgjJegiaW8BiaVZjTTHCq0nRYKJR2vt+a3o+0fXo36OumNdgiG4LIHqw+tvYXh2dt7VzxN7Wj4iIiIg6pF8FpdatW4fJkyfD1hiMGTNmDDZs2NDqvKFQCKEmF9xutzv6n6FDW1/4aacB//3vnueZmW1fqB53HPDZZ3ue5+UBNTWtzztxIvDtt3uejxoFbN/e+ryjRgE//bTn+aRJQBvrh9xcoLR0z/NjjwXWrGl93vR0oLp6z/NTTwVWrmx9Xpst4YJ794m/xdT/fYKprc+Nt78vwxnjojVOrnvhfjj/+3YbcwKzn/0injlz9ovzkX/1u23Ou3FdMV7bHu1ic9HihzHm86Vtzott24C8POi6gPuGOUh56om25y0qAg47DAAg/vIXTL3//jbX7fobn8G6nQfh9DFDIf/f/0G+5Rb8uY15Z132CIp+MR7DM5Iw5fOlOOO5uW02YdaMe/HlIUdCkiT8dt2n+MuyBW3Oe9P0P+PDw44FAPxm4yo89p/Wl/sXALec9ie8Oe5EAMCvStbi+f/c1+ZyH5r6R7x99DRMnzAMhSU/Arm/bnNePPwwMGdO9P9r1wJHHBF/6TAA85vM+t8zr8T/LvwjIEkw/7wR9/y55bDPbzb+u+TYGXjud9cAADIbKvHK3PPbbMJrk07HU+fcgIimQ6mpxupH2h5O+pMjT8Uzl94Fs1HGhAwzbp8xqe11mz4deP31Pc+T2u52lPabExE65754psidlx0PUyjY+sztOEcc1vgIjhuPn5d9vCcbZnh+/BxhwJ7tBQDF6Qdh6qxnYFBkGBQFSxfeiIKaHcCDaLHsBTnDMO+p9/BjmQu7XEEseelWjNm1pdXm1tmSMfXOpYg0Zui98Po9OHJHUavz+o1mnPPoh/EA3TWP3ohDv/+i9e0AYML978PdWB9twdKHcdrPq9qc98K/foTUVGe0btLVfwBefLHNeVFVBWRkRP9/443AU0+1PW/jOcITVPG71/+Osz75d5uznnblUygZnId6fwRHvPtPPPFB2214Yt6/UJWSj/OPGAbt4Ucw/dW2zz1/ufnvyDr8l5AkCUd8+MZezxH/vO1v+H700Vhf5sJBy15D4XP3t71ur70GnHNO9P9vvgnMmNH2vC+8AFx6afT/778PTG3r7AfgySeBa6+N/v+LL5D/61/juTZmfffiG/DPX06PBhO3/wxMPrLt5d5zD3DvvdH/b9wIFBa2Pe/NNwOPPBL9/44dQH5+2/Necw3w979H/19TE/0b3pbf/x5YtCj6f79/r9/7jpwjiIiIiKjj9j0MWh/idruR3+SiVJIkKIqC+vr6FvPOmzcPTqcz/hg2bFhPNvWAoKp6tE7UXixcWQJVjWZdOVvpztNUSmMGTiCsQdnHkffs5yVYX+ZCitXUajeh5orKXXjgvxuwamvtPueNafDvJaMKQE6KPV4jZV+SLQYUDnXGuyDtjSJL8W5Rut5GJlwnCdG+Zeam2pCTYsMQp2Wf83aELxQdgbDGE8KmXe69zhvRdADtW39diMYC51rTHn+tclgMuP20Q3Df7w7Dracc0s6W75tBluLdN7uSxSBj7LAUDM/Yd30rAcBokCFL0c2wt3J6JkXGXb8dhcuPyUNmsgXKXmYWIlrjTG7yfG/OnZiD0TlONATCCET23l3LHVShyBJGptsxYh+1hmL1lLqrblLTIv9tUWQJZkVBssWwz3NarC7U2GEpOGZk+l7njWiizWL8zcmyFO0WmpkEX6jvd4er8YZ6tQg7EREREfVfkhD7uv3oO2699VZEIhEsWLAns2TYsGFYvXo1srMTi/22lik1bNgwuCoqkJyc3HLh7L7XYt6VP1dhzsvfIMkAWI2tFJCOqKiTTPi/c8fhuIMzo926NA2qquOut4qwcbcb+en2aG0YSULEbIUAUFzlxbhMK84fnwV/WEOS2YDcxq55ui4wf8VGrK0OY+TgaH0gJRKGrKkQQqCk2ofCbCduPeWQ+M1PUV0YT3y6FXW+MHLsBthlHcGwjt3uAAbZTbj6+BE4bGjjTW6T7ns/bq3CI8uLcFCaDUorN1IhxYRt9UHc8dtDMXawLaFboK4LfLihEs+s3IqDUm2wJdvjXWhkNQJFjUDTBXbU+jF9Yg7e/r4cP5a5ooEYixmabICq6TALDRkWCbIkIRTRkOEwI8VqxLfbG6DrApLFDKEoEAIQqgolEkaS2YCpY7Jw26mHYuNuNx5bsQlfbauHqhigG41QJMAEHQiFEC07Ht3dE3IHIT8jum9dqoS6iMB9vzsMw1Ot8S55rWqj+15zP1W4sHR9FTbXhbC5yoNgKIJcq4Tc9CQMagxICiFQVO5CvT8MjyZBN5qQZDZA0nWEPD74wxosRgVjh6VgcLIZwbCOXS4/yrxhKBYrhqXZ8HOFG05EYJRlAAKuYASpNjPGDXPCF9JRF9Zw19mHR2uidaZrTjO6LrC91gdXWMdL31die50fIzOTYArt6UrU4tg0KO06R+i6wPb6ANyScU+mVDAQ/95/sbkKc95YD5MCeIM6ZEVC2LSnvYZQAJoqMHZYMkwGBXecduieLrGN54iSai/m/OdH7N5dh0hYRUQTMDUZtU8XQETTELHYoEiAJ6TBHAnBKIl4ACfWdU+SgIMzk/D6TSdClqVo99oGL1zeAF78cjt8YRWbdnmg6tHPUCQJXqMZoYgGWZZw+/F5uOyo3Bbb1htSo+eBYRmQYxHrvXTJAwDdYkVpnT9aA03SkOc0tx0Qafze67rAbUvW4OP15VB1AbOyJ0AsAERUHZrVAqvJgMHJVtx38ki8unobNu1yI6/xXLZnmwj83BBB4UFpuPO3h0JWI9BD4cT1aTyvbav24p4Pt8JptyLJYoifIwDAE1Dx7fZoMP2IvDQkWQzQjCboSvR84vcG4PP6E/dtUz3UfS/2vf+pwoW3fyhHSbUvHpTLG5KCaUc2jqq6l3MEgAOi+57b7YZz6FC4XK7WrycOAG63G06n84Bex273StsZvV3igle7d/ndqZPb5oedDV3bjh40blhKty6/r2ybvw1+cN8zNfP8pXvJaN9Ply/6dt8z7YcOtZ3nBBqA2ns90a+676WmpqKoKLFLicfjgcnUMjvFbDbDHLvobMpuTwzQtKU983Rm3qaBpK6c19r6aFj7M2+1JwS/bITdZkJQbpnapBt1qN4wqj2NNzaNF+4GABeeMApPfLwFO31hZFnN0WHNQ3uGY68KC9z7yfYWI1DZTAo2unVkpdjiN3+a0QTNGN3HqZlmbHCHURoEhmfYo8WnfyhNKD6tAzDZgGHOaPHpNzY14NARWS1uWJOSbUCSHW7Z1Oqobf6guqfgssm05+YI0RTDguFAUlENdKtpz00hAN1ghG4wwhtUgSQJo38xFPm5mbjh1R9QWuuDoktQIGA2KnBYzBAGGXWBCBxJNlhTrDhnci4qP9mCnys90HVAhogmB8kyJJsdB2Un43dH/wKyIwmHOZLw1LBMHPnQxwgGVViVaEF0IWQEIUPTBXQBmA0yhmanISLLEEKg3OWNdrVJs0drVrX3GFaUNuc9rMCOQ0dk4Yst1fjrR5sxyObE4GRztNB9k/lycsxAvR/D7SZs2u2GKxB9VTOYYbLIGJ+XitQkc3w/HuRMQkNZA7whDVWuICI6ELFaERSIFlW3mTE024mI1QTFLOCr8e0ptix1YN2AFvM2Hy0toumo9YXgD6sYkeGIHtdhDbvcAaSmOnH6UQWQHa0EDlppQ3tGYvvl6Fykfr4T22p80A0CJqMSTxQTQsAtmZDkMGDwkDRsrwvArZhbfFZemh2js5NRWuOFOyLDZJGhyXsCMeGIBslgQprNBFkS0ZHlYEYI0UMjNvoeEM0IvPTkwvggB8MzkoCMJKzb2QCXsgub3SH4DNHvewRABNHzgWxS4A9rePmHKlzy60Pi75cB5Le2vYBo0KC1c3h8223c67ZrjSxLuPi4Anyxw4UGdwgRowxFlqNBN02HbJFgUmTYTEY4rUY4ByXhoinRc1lZ03NZbJ87rHuyg0wmyCZTq+uTa7VhxJCaePH52DkCALyRIBokM7KcFpidDkSaZXKZbRbs8mut7tsWDIaEc1GXzdvkex/7nrc5KMJezhEtyHL3zNuR731nzhFa12ZLEhEREQ1k/ar73qRJk7B69er489LSUoRCIaSmpvZiqw5cGQ4zDIqEkNp6VlVY1WFQJGQ4Wt44xrrhxLr4lNb40BAIIzvFCjSOHpdiNSEv3Y4Uqwnry1x44uMt+GFnQ3SEpza6uVhNCkKRPSM87W2YcgBwWIxYu6MOX2ypbtGtLTZK2S5XAM0TBmOjthVkOuKj7zXXkfePyUnBghljkemwQJElpFgNSLObIEsSXIEIzIoMq0lGwWAHThw1GPPOHoOTDxuMVLsRRlmCQZaQajdhyiGZuPO3oxJuvC0WA646djiMsoSgqiOs6dBF9F5LF9FuXnnpdghI8AZVFFd5u62rjSxLSLGZYFIUZDjMre4TqzEaLDvlsMGYe2Yh5p41GjecWICDhzhw9Ih0pCYlHk+SJGFEhgNpdhOGpdmgQ8AVUBFWdaTZzSjMdmJQN4zcVlQePSZj3Ujz0u3IGWRDktkAb0hFWb0/flyPyUnpULez1pbd9HtQVO4CABgMMmYdNwJWo4KILhDRoiPXqbpAIKLDIEsYNSQZQVW0ud6yLGH6hGHIclqhI9ptUtMFNF0gFNGgNR4jgYiKer+KwckW2Bu/f5poLJQvAdkpVjwwrRBnjMtu8RkOiwGBsAZ/WIPJILfsYSkAgyKj2hfCqq1t1N9rp/Zuu9bouoDdbMB5k4bBbFQQjOjRbaDrMBpkmAwy7EYl/l3MS7O3eS7ryD6PjbiYajehuMoLbzA6Yp83qGKXKwizQUaW09Jqf8yuPKZb2x4l1V6s29mAkmpvu7sTx0ZnbW+3UyIiIiKitvSrTKljjz0WLpcLixcvxiWXXIL58+fjhBNOgNLaaHo9SNdF278a92NHj0jHUKcFO2r9sBhlSNKeGKYQOtyBCHLT7Dh6ROu1VNoajr28IRDPagKiw6iPNEezmlYV18JsiNbtaS17qfkNWlvDlNf5wthW44UrEIEvpOGvH23BZ5urEzIpYjeK5fV7RlWLZ0G4AvsM3HT0/WOHDcJtpx6Cxz74GfX+CEKqgFGR4DAbYTXJyBlki89fmO3E/503HiU1Xmyu9AIQ+MVgB4ant34DeO2vo6MgPv/FNriDKsJChyxJSG6sS2M3G1Ba44uP8HbW+Oz4dujq49dhMcRrLzXfh/W+MDbtdqMhEMFLq3cgxWbEyMwkjM52wiDLsJlbPyVZTQqMioyrjx2BZPMObNjlxvAMO5ItxviNfCwQGM8A2w+6LvDG2rKEDDwgeqyOyUnBlkoP8tKTcNHkg+C0Gju0zVpdthAQEEi1GVHhCuCN78owKisZsizhjHHZ0IXAA+9sgCsYgapFu3smWQwYNSQZB6XZUFzl3et6F2Y7cd1vRuLOt4rgCakIa9FAsyJJUORoJp3FoMAoCwzPsCPDYYKmC+Sm25CeZMFRI1Jx7MjMeIZTbD2afrdTrAZouoC52elYNAbRzIZo97l4ZmUn7G2/xM4hS9eWx7ddU80z03JSrChrCEAXAiZFgcUowWZq+V2Mbb+m57LOfE9iwa1YGyrd0QyvSXmpqPWFUdEQDW43DeR25THdXHsy9YiIiIiIulu/CkoZDAYsXLgQF1xwAebMmQNN07CyrVHkesiBfGEfy9J4cPkGVLlDSLYaYTLICKvRgJTVpOCq44Yn3Kg2F/tFHQBKqr3YWu1rNatJkiRkOa2odAcw2GGJ1u0xJ7V6gzY62wldCKzb2YAGfxgmg5QQAKnzhVFU7kJI1WBSZNhNCgbZjFhf5kJ5fSAhu6GtG8X2Bm7a+/6YM8ZlY3i6HYtWlWJrjQ+6LuC0GlEw2NFiflmWMDLTgZGZjnbtr2t/XYArjx6ON38oR3lDANkpVpw5LhsGg5zQ9oMG2bCj3o91Oxuw2xXA6pI6FFd33fEbyyCLdVWK7cN6XxjryxrgCkSQkWzBIUMcCEZ0rC9zYfNuDyKavs9gZIrdhJnH5OOJj7eg0h2CLMkdCiS2194y8CRJwtAUG6o8QTitxvjx3Vxbx0zzZdf7wiip8cIdiCCsCuhCoMq9C0fkp+LU0VkAgDMPz8HwdDvue2cD6nxhDE4246BBNgRV0e7MtxNHDcFXJbX4dltdYwFvCRUNAXhCETitRriDKtLsZgx2WGAzKSip9kGRZNx28iEtvuOtnfdUPZpxFdJ0mCGjMdYGVReQZQkWg4yIFs3QWrezoVOBnX3tlyynNT44QdP9EsuuqvOFo8Fjp4JA2AKDIqHeH0GyxQCzQWnzuwgknss6q63g1oZdbjzx8ZZOBcc7o/XtobV6jiQiIiIi6k79KigFANOmTcOWLVuwZs0aHHXUUciIDQneCwbChX2sq84/Vm5FhSsId1CFQZGQm2bHVccNb7UrT1vaymqKsZoUVLoFjilIh6dod6s3aAZZQq0vjPuWbYArEIEsAd6QijpftDsNAGyr8SKkaki2GOI32kOSLRicjFYzKfaVBbG3wOOorGTYTAp+N3Yo3IEIkq3GfWbOjM5JwSPnjN1n1kVnMphMJgXnHnFQi+mxm+michf+8l60Fk+dN4zd7iAUWcIhQxzIS7fv9/Eba/OYbCc2V3r27EOjHK8f5bQZ8YvBDhgUGUmKjJHmJGyp9CCk6tjlCrQZjIxli8iy1KFAYGe071jd0420udgxs6XSA1dAhSJH98GlR+VBCMSXXd8YQPWGVWiagKpHu9Z5ggL3vvMTwpoe/46NHTYI959RGF/v7XWBDq13rBtfRUMQdb5wtMudqsFsUOAOqrAYFKQlmbB2Z33jSIc6drmrcMvSH3HZ0fnx5bd13tvl8sNslBGO6NBkAaFHA0Vmo4Iks4I6XxhWk4J31lXgjbXl7QqANv8OuAKRDu+X7sx664zWglsdDW7vj/3JNiMiIiIi6mr9LigFANnZ2S1G2+tpA+nC/oxx2fhtYRZWba1BtSeEtCQTclJs8Ec0lFR7230Tt7duXcCebJixw1JQMNjR4gYtO8WKSk8QxVUe+MMaAmENEU0gpGrQhUAgoiEvLQmuQAQmRY7faOen2wFJggS0mUnRVhbE3gKPG3e5kekwR7viNQtW7W17tCfYtCeo4UWVJwgIIC/dhmuPH4mxBw3a57ZuTdN1GZJswW5XEEJEM1dKqn2wmQwYZDd1+vhtrSh4SNVQVu+Hpgs0NGZI/WKwA6n2PUXjY5lHZfXRoEZ7skX2pztVe7Z/e4/V1ur8xLZzWb0/WmcpoiGiRrfxmtI6XDw5N7rskIqSGi+84WgASNcFDIoMWRKQJQFPIILHPtiM4el2jG4MuO5vN7KmwY/vd9TDF9JgNylIs5uRlmTCjlo/gqoGm8kAi1GGO6BiY0U0i2f2lAKMykrey3nPgTpfBBUN0VFDk8xGmI3RLnt1vjC0xqzAQfY9xcL3FgBtLRic4TC3K6Ou6X7piqy3ntAVXQTbo7PZZkRERERE3aFfBqX6goF2YW8wyDju4Mz96q7YVrcuoPVsmOb1qP61ege2VHng8kcQ0nTYTAbYzRIimoIabwjVnhAMkpRwo52fbo8XwQb2neHS1N4Cj2mqCV9vq0NJtQ9H5KXC5jS0K8uoPdsvFtQorvKi2htCMKJB1wW21vjw5dY6XH388HgNqfZqvi6eoApPUEWSxQijEi22vq3Gh0E2Y6eO37aCdxUNfliMCiblp+LjjVU4ZEg0Q6q5WM2o6eNz8GO5q13ZIp3pTtXe47cjx2pr27ms3h/N6lEbj1OTBFXXUecNY/FX2zHuoBRsq/HB5Q9D0wR0XcBokIFY/SWjAodZQb0/jEVfluKR6WPjwYn97UYWC35ER0ncgkE2IwY7LFi7sx5BVYt37YtoOkwGGfkZdlS5Q1i6thyWI+W9nvcOG+qE0SBB1QRqfWEEAyoMsgSrKdo17sj8tHYF8Ns6nrbX+FDrCyGkahiTk9Ku/bK/WW89qSu6CO7LPreHUcb22gjWbq8HgC4NjB2o9ReJiKiHvHJuh2a/rrKhQ/P/bfCDHZqfiLoGg1Kd1J9udLrK/nZX7Ghh8Ob1qIqrvPCHNYQ0PX7jDAAmQ3SktwZ/BJnJFtjN0YyfIcktR7PqyEhWbQYehcC2Wh8kKToqmQCgyNI+s+Tas/1imSjFVV6U1/uhCcBkkKEYokENf1jFk58UY2iKFWcentPOPddyXWIjsBlkCYAEmynaNcoTUuGwGLsseFcw2IHiKi921gWQYjUiGNGR1EpQqmmW3Bnjsrula2NHjt/OFsEvrfVhS6UHgbCGkKoj2WKMbw+jomCQ3YSGQAT+kAqbyQBPKDrym0GWIXQRr7/kMBtgMsgwyjq2Vvu6PLgtyxJ+VZCBzzZXY32ZCzZTBO5AtE2ABCEE/GEVaXYTki1GKJKMLVUebK707PO857SYcNspB8MTUlHtCUHTBd5ZV4FB9pajMbYWAN3X8RSIaPCGtHbvl/3JejsQtXcgghe/2o6311V0WY3EA7n+IhERERF1XtsVqmmvml7Yt+ZAu9FpfqOYZDHsCcRkJqHOF8bSteX7HFK8s8Ore4IqXIEIAmEtfuPclCLLMMgyvCEVIzKimUDNWxLLpCjIdLRrJKt44NGUeAPuCalwB1QkmQ3QdCDSOJIZ0PImu6Pbr6TGiy2V0QwpTURv8g2yBEkCjIoMq1FBWNXx90+Koao62qv5uhgVGYosQW3cXwZZgqYLRBqX2SXBOyQWsM90mLHLFR1hrKnm+2Vfw80XlbvwwH834J5lP+Ev/92Ie5b9hAf+uwFF5a4229iZ47czx2r0OFXhj0SP0+bbw6jIMMoyan0RTB09BDaTAk0HNF1AE4DZqCDVZoLZqEDVo6Mz6rroluB2LPCWajehpNqHsKpDlqLHszsYgdmoIC89GhSymhSEIjoAqV3nvRS7CccdnInpE4fhkKxkhDXR4nsUE1t2bB33dTyNyHAgzW5CbqqtXfsllvXWnmNvIGhre8QGIqjxhDDIZsIhQxxIsZqwviwazN3b92tfYgHh9WUupFhNyEu3d9myiYiIiKh/OzAiJr2gs917+quu7K7YmdopDosBsiwhognYzS3n03QBo0GCLrDXQukdGcmqrYyCiBrNMjIqEhRZgrFZ5k9rWUbt3X6bG2tIBSMaTAYZzVspS9GsrN3uIFZtrcFxB2fucz1aWxeHxYBkqwF1vjCSLUaouoiui0Hu8PHbFQXs27tfOput19njt6PHqsNigCIDEVXAbmo5T9NA0+icFPyqIB0f/FSJJIsBJkWBUZEa2xfNVHKYo4Xzuyu4HQu8vfC/bdjlroI7oMJkkJFmN+H/27vz8KjKu//jn1mzTVYgARKWhFAthqAgQoPQPkJdCih1X/qoWKs+Kmip1A1rXQq41lqsQrVSrEu14F7craICPyOCxKASNUAie5JJJstMJnN+f6QZCVnIhMyWvF/Xletyziz5zpxDPOcz9/29h/d3+Ht/tYRNP8hwBPx3L9CRSp0dT4bR3Aze7fXpf36YriMHJqrW3dTpfunuqLfeqt3P4xALERxOj8S+1H8RAAAAgWOkVDcdOMqgZI9LrgavmnyGXA3eLi/RHsl8PkPf7HVp044qfbPX9f2qV10c7XAohxoNc7Dh/RI0on+CGn2+ViOTJPmnGsXbrUqOs2rMkJRujcZq73e2N6LAZjXLbJJq3V4lx1nbBAbtjTLqaNRVi+9HohiS0fz5W0xtPxPDaA5RPF6f3t6yWyV7ag45Oq2992IymZTd3/HfldcaVdPQqMRYq0yGAj5+uzpq8HD3y+GM1uvq59/e8RvIsTq8X4JyBjjk9fnk9R08ku3A47Q5bJo9KVv9HHbVNnil/47ta2zyyVnfqBiLWXF2s0ZmJGpoanyrf49d2eddlZeZrLvOyNcJR6YrIylGY4el6Jihqf5A6sDRRDn9HQH/3Qt0pFJHx1NFrUcbtlfq49IKlVXWafmHpXpy/XZZ/rt/Otsv3R2h2Vsd/Hl8savGvxBBXmZKm4UI2hv92VWBBMIAAADoewL6+r2pqUkLFy7ULbfcIklqbGzU8OHDVV5e7n+Mz+fTqaeeqldeeaVnK41AoVzGO5R6atWrnmQ2m3TxpOH6eFuFKlwepTnsspjNavI1X+jHWM2Ks5n1g4ykdhuld6epbkcjLEzNuZF8RvMFd0+OFvlBRqKG94/X1/tq5fX5Wo3C8jb51OD1qSWP+Ncn5Xrriz06dliqLpsyot3j7cDeSxOz+6msos7/XpLjbBoxwKEvdlXLMDX3rqqs92hYvwRNyu2neLtFvv/2OOrM4TSwD2S/HM5ovVD1FTKbTbq4YLgKS5uP09QEu2wWs7wtx+kBQVPL+/7NiUfovje+VGVdo2xms2wWkxJjbIqzm5WVGq8xQ5L1h9VbgtqLx2o165JJ2Xrw7a3aU+2WxWTucDRRoH/3Ah2p1N7xVFHrUVG5U25vk5p8hjKSYjUoKbbL/eyk0K1uFy0O/Dw2bKvU39du63Qhgu72SOyL/Re7yuPxaM6cOVq6dGmHj7nnnnt03nnnKSur6z0EAQAAoklAV2Bms1mPPvqofvvb32rKlClav369bDabpOYTp/nz58tsNmvNmjVBKTYS9bYLnZ5c9aqnjc5K8V/AV9U1ymo2y2Y1NTfmtjVfwHfUKL27OroA/1FOP+2ubtD+Wo/sVsshpwN1NbjJ6e/QVT/J1UdfV6jO45XZZJLZ1Dztq8HrU8tAE5tZSkuwqc7TpPe/2qc91W4tmDGq1YV5e+FiarxdcfbmVd1a3sv00YM0Maef6jxefbB1n/ZUN+iZ/7dDz39a3qXw43Aa2AficC5uQzndNv+A47SjoOnAz+O0ozOV0z9Byz8s1df7auXzGUqOs2lkRqLGDEnWq5/t7PbiAoEIJGwK9O9eIK998PE0MClW3+x1qd7jlckkJditGjHAIUecTbmx1oCmf4VidbtocuDn8eKm7w65EEF3QlsazXfMZrPpiSee0I4dO5SZmakjjjhCBQUFOu6442S1WvXmm2/q9ttv1ymnnEIoBQAAeq0unwW2TPmxWCyyWq3avXu3pOagSpLuv/9+zZ8/X5Jkt9s7fJ3eqLdc6PT0qlfBcNrRmcrun6DlH5Xqm70uNfmk5DirfpCRFLTRaR1dgBfvrD7kRXbrkUppKutCcDNmaKr+7yc5WvJOiRoam2T5bxPylkDKbJJSE2IUY7PK/t/pd9/sq9XKT8r8F+YdhYvfVdUrNcGuX0wYqoHJca3eS6vH2wMLP0IxavBwLm5D3Veos6Cpvc9jdFaK7jlrTKtjbGhqvP6wektIe/EEEjYF+ncvkNc+8Hj6rMyp3dUN/+11FaPs/glK/e/0skD72aF9wQxt+1r/xUCYTCb169dP11xzjXbu3Klvv/1WCxYs0FdffaVzzjlHf//73/XEE08oLy8v3KUCAAAETZdDqf/5n//RBRdcIEmyWCz+MKqFw/H9xcDBU2sQeQ4MS1ouDruy6lVZZZ2GpcVrj8vdI8FDe3Uc6gI7PytF9545JqSj09q7AD/URXb7I5VsikuJazVSqb3P76r/GanBKXFa8k6Jdjkb1NjU3F/HZjEpNd6uxNjmEYomk0nxdqvqPF59Vu5U6f5aDe+XcMjGwuu/rdSC6QNlNpt6rBFxsEcNHu7Fbain27YXNHX2eRx8jH2z19VjiwsEIpgheyCv3XI8vfb5Ti1592sNS4tXSpxNOuiz6MvTv3pKMENbGs239fjjjyszM1NTpkxRXFycTjrpJP99hYWFuu666/TII4+ooKBAs2bNCl+hAAAAIdClUMrn8+nqq6/utO+Bx+PR448/Lp/Pp6am9hseIzK0F5bkpjs0OjP5kNOjbBazfvGjYUqOsx128NBRHV3plRMpo9M6qqPTkUrxNv1iwjANTI7t9PP7+TFZmjl6sJ5YX6q/vv+NXA1N6uewtwmELebm1QcrXM0Ni32GEVCY0ZMrKwZrv7SEl/mZyfpqd023L25DPd32cD4PevE0f35HDkxSvwS7rGZzm0BK6tvTv3pSMEPb3tp/sbs++eQTPfDAA/rqq69kt9v1u9/9TiUlJVq7dq0GDx6sSy65RKtXr9bFF1+sm266SQsXLgx3yQAAAEHTpbN4s9msM888U2eeeaZycnLafUxjY6NefvllGYYhX5tVpxApOgpLNpc59dWumi41M0+Osx128NBZHT3dKyfUujLyaP23FVow/YcdhiEHjiDL6e/QoJQ4bdtfpyaj9ZKZbm+TnHUeNXh98nh9+tsH32pgcqz/c23PwWFGpIcfB4eXjU0+ub1NKqusk81iDvjiNlICzUOhF08zpn+FTjBD297Wf/FwLFmyRJJUVFSkFStWaP369XrnnXd0/fXX68477/Q/btmyZcrPz9d5552n0aNHh6tcAACAoAr4aqa0tFR5eXlyOp167733/NtjY2O1atUqSVJ6enrPVYgec6iwZOvuGrm9Pu101gf14q+npotFqsMdeXRwCGO3mlTvaZLPMFTrblRKvF2SSW5vk/a73GpsMmQxS4OSm1ck+3ZfrXY5G9Qvwa7M1Pg2r39wmBHJ4UfHI87qFGuz6IxxWTp6SEqvvLgljGnG9K/QipTpm73Zb37zG1VUVGjUqFGqrKzUihUrdNNNNyk3N1czZ87URRddpFNOOUUnnHCCbr75ZpWXlxNKAQCAXqvtMjsd2LhxoxYuXKghQ4booYceUmNjo371q1+poqJCmzdvltPpDGad6AGHCksGp8Qrxto88qRkj0uuBq+afIZcDV6V7HH12MVf6f5abd1do8RYmypqPaqub5Tx3y7eB4c20cg/8sje8cgjd2P7I49aQpjNZU6lxNk1vH+CUuNjJEmGIbm9hipqPfJ4m1RV61FjkyGTSUqOtSs3PVGOOJuOGpwki9mkL3fVyDho1GJLmDEyPdEfZrSEHzud9f790NnjQ+Xg8NIRa5XFbPI33nd7fSoqr+6VgZT0fRiTlmAP6r/HaNAy/Wt0VrKq6j0q3VerqnqP8rNSonpUJfqmhQsXaurUqXK5XLJYLDr//PN1yimn6OKLL9a6dev097//XSNGjNAxxxyjyy67TCeffHK4SwYAAAiaLg99uOKKKzR9+nRZLBb9+Mc/Vv/+/fXVV18pKSlJM2fO1JgxY/yPZfpeZOrKNC2bxawzx2bps3Jn0Hp/bNpRpa27XZJJ8vkki9mkpDirsvs7lJZgD/t0scPV3ZFHnY0gy89KkVSlhsYmNTT6VFnXqHpvk2wWszISY/XDQUn+FcnMZrOOHJikz79zqmhntbL7OTodWRKpI1F6stdVtKIXz/eY/oXe4swzz5TdbtfevXu1a9cuHXnkkVq5cqV+9KMfKTU1VS+//LLOOussvfHGG9q1a5cGDhwY7pIBAACCpsuh1Nq1a2UymfT4449Lkn9ERb9+/fTRRx/pZz/7me655x5dd911qq6uDk61OCyHCkvq3F41GYZibGZdMGGoJKnW3dSjF39F5U79a0OZ6hqblBhjVUKsRV5f8+ifWrdTeZnJsv+3T1C09srp7rSrrqx+WFnn1qWTs1W8s1orN5Rr5ACHUuPtbRpAD0yO1X6XW9n9ElRV3/lKf1Jkhh+R3usqVAhjvsf0L/QGZ599tux2uz777DMtWrRIxxxzjM444wwdf/zxqq6u1kcffaSSkhLdfffduvbaa/XMM8+Eu2QAAICg6fJVv8lkks/nU2NjoxobGxUf/32vmkGDBumtt97Sj370I6WkpGjv3r1BKRaHp7OwpMLl1qc7qmS1mLT8w22tVsLrqYvAlpFA7kafBibFqLKu0T86KynWpuqGRn27z6XEGKvGDEkN2nSxA5uIB+MCv7sjj7oWwhhKjY/RyUcN0tqvK2SzWDpckSzNYdc100bKbDJ16b1GWvgRyb2uQo0wBug9Jk2apHPOOUdnnHGGZs2apW3btikuLk6ff/65hgwZovnz5ysrK0vnnnuu7rvvPm3dulUjR44Md9kAAABBEdDVXFNTk0466STZbDZ9/vnnampqUmNjo6TmEVPPPfecCgoKNGbMGB133HFBKRjd11FYssvZoM/KqiRJRw5M1sDkuIBWwutqyHPgSKD+jhgVlTvlrG9UvN0qq9kku8WsXc4GDchMDtp0sYObiB8YvvXkaKDujDwKJITp6misnP6OgD7HSAo/aPQNoDd6++23dcMNN8hkMumLL77QkiVLNGnSJK1bt07x8fH68MMPtXPnTknSueeeq48++ohQCgAA9FoBhVI2m02PPvqo/7bZbNZTTz3lvz1mzBg9/fTT/qAKkefgsGSXs0nlznrFWM06ekiK0hzNTbW7uhJeICHPgSOBHGar8jKT9c0+l6rrvar3GTKbpDi7VWeM69mA6MBa21vJravhW6ACHXkUSAgTqX2gelIkvMdgj6oD0PecdtppSk9PV2Njo04++WTFx8frlVdeUXp6up599llJzSPQJemII47QjBkzwlkuwmDjjqpDPubPyz/u1ms/dvH4bj2vq37Zhbrm7K4Kag1Ad83ZvSCgx2+8K0iFSDp6SErwXhyIMAHPe6mqqlJhYaGmTZumgoICPfzww1q9erVOOeUU+Xw+Pfnkk3r++ef16quv6qc//WkwasZhOjAs+WJ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      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体支持\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei', 'Microsoft YaHei', 'DejaVu Sans']\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 1.读取数据\n",
    "import pandas as pd\n",
    "df = pd.read_excel(\"D:\\贺之恒的python\\pppython\\python商业数据分析\\@Python大数据分析与机器学习商业案例实战\\第9章 AdaBoost与GBDT模型\\源代码汇总_Pycharm\\产品定价模型.xlsx\")\n",
    "df.head()\n",
    "\n",
    "print(\"数据基本信息:\")\n",
    "print(df.info())\n",
    "print(\"\\n类别分布:\")\n",
    "print(df['类别'].value_counts())\n",
    "print(\"\\n彩印分布:\")\n",
    "print(df['彩印'].value_counts())\n",
    "print(\"\\n纸张分布:\")\n",
    "print(df['纸张'].value_counts())\n",
    "\n",
    "# 2.分类型文本变量处理\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "le = LabelEncoder()\n",
    "df['类别'] = le.fit_transform(df['类别'])  # 处理类别\n",
    "print(\"\\n类别编码后分布:\")\n",
    "print(df['类别'].value_counts())\n",
    "\n",
    "le = LabelEncoder()\n",
    "df['纸张'] = le.fit_transform(df['纸张'])\n",
    "print(\"\\n纸张编码后分布:\")\n",
    "print(df['纸张'].value_counts())\n",
    "\n",
    "df.head()\n",
    "\n",
    "# 3.提取特征变量和目标变量\n",
    "X = df.drop(columns='价格')\n",
    "y = df['价格']  \n",
    "\n",
    "# 4.划分训练集和测试集\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)\n",
    "\n",
    "# 5.导入多种回归模型\n",
    "from sklearn.linear_model import LinearRegression, Ridge, Lasso\n",
    "from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.metrics import r2_score, mean_squared_error, mean_absolute_error\n",
    "import numpy as np\n",
    "\n",
    "# 创建模型字典\n",
    "models = {\n",
    "    '线性回归': LinearRegression(),\n",
    "    '岭回归': Ridge(random_state=123),\n",
    "    'Lasso回归': Lasso(random_state=123),\n",
    "    '决策树': DecisionTreeRegressor(random_state=123),\n",
    "    '随机森林': RandomForestRegressor(random_state=123),\n",
    "    '梯度提升树': GradientBoostingRegressor(random_state=123),\n",
    "    'SVM回归': SVR()\n",
    "}\n",
    "\n",
    "# 存储结果\n",
    "results = {}\n",
    "\n",
    "# 6.训练和评估各个模型\n",
    "for model_name, model in models.items():\n",
    "    print(f\"\\n=== {model_name} ===\")\n",
    "    \n",
    "    # 模型训练\n",
    "    model.fit(X_train, y_train)\n",
    "    \n",
    "    # 模型预测\n",
    "    y_pred = model.predict(X_test)\n",
    "    \n",
    "    # 评估指标\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    rmse = np.sqrt(mse)\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    \n",
    "    print(f\"R²分数: {r2:.4f}\")\n",
    "    print(f\"均方误差(MSE): {mse:.4f}\")\n",
    "    print(f\"均方根误差(RMSE): {rmse:.4f}\")\n",
    "    print(f\"平均绝对误差(MAE): {mae:.4f}\")\n",
    "    \n",
    "    # 存储结果\n",
    "    results[model_name] = {\n",
    "        'model': model,\n",
    "        'r2': r2,\n",
    "        'mse': mse,\n",
    "        'rmse': rmse,\n",
    "        'mae': mae,\n",
    "        'y_pred': y_pred\n",
    "    }\n",
    "\n",
    "# 7.比较模型性能\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"模型性能比较\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "performance_df = pd.DataFrame({\n",
    "    '模型': list(results.keys()),\n",
    "    'R²分数': [result['r2'] for result in results.values()],\n",
    "    'RMSE': [result['rmse'] for result in results.values()],\n",
    "    'MAE': [result['mae'] for result in results.values()]\n",
    "}).sort_values('R²分数', ascending=False)\n",
    "\n",
    "print(performance_df)\n",
    "\n",
    "# 8.可视化模型性能比较\n",
    "fig, axes = plt.subplots(2, 2, figsize=(15, 12))\n",
    "\n",
    "# R²分数比较\n",
    "axes[0,0].bar(performance_df['模型'], performance_df['R²分数'], color='skyblue')\n",
    "axes[0,0].set_title('各模型R²分数比较')\n",
    "axes[0,0].set_ylabel('R²分数')\n",
    "axes[0,0].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# RMSE比较\n",
    "axes[0,1].bar(performance_df['模型'], performance_df['RMSE'], color='lightcoral')\n",
    "axes[0,1].set_title('各模型RMSE比较')\n",
    "axes[0,1].set_ylabel('RMSE')\n",
    "axes[0,1].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# MAE比较\n",
    "axes[1,0].bar(performance_df['模型'], performance_df['MAE'], color='lightgreen')\n",
    "axes[1,0].set_title('各模型MAE比较')\n",
    "axes[1,0].set_ylabel('MAE')\n",
    "axes[1,0].tick_params(axis='x', rotation=45)\n",
    "\n",
    "# 预测值与实际值散点图（最佳模型）\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_y_pred = results[best_model_name]['y_pred']\n",
    "axes[1,1].scatter(y_test, best_y_pred, alpha=0.6)\n",
    "axes[1,1].plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'r--', lw=2)\n",
    "axes[1,1].set_xlabel('实际值')\n",
    "axes[1,1].set_ylabel('预测值')\n",
    "axes[1,1].set_title(f'{best_model_name} - 预测值 vs 实际值')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# 9.特征重要性分析（适用于树模型和线性模型）\n",
    "print(\"\\n\" + \"=\"*50)\n",
    "print(\"特征重要性分析\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "# 随机森林的特征重要性\n",
    "if hasattr(results['随机森林']['model'], 'feature_importances_'):\n",
    "    rf_model = results['随机森林']['model']\n",
    "    rf_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': rf_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"随机森林特征重要性:\")\n",
    "    print(rf_importances)\n",
    "    \n",
    "    # 可视化\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(rf_importances['特征名称'], rf_importances['重要性'])\n",
    "    plt.xlabel('特征重要性')\n",
    "    plt.title('随机森林特征重要性')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 梯度提升树的特征重要性\n",
    "if hasattr(results['梯度提升树']['model'], 'feature_importances_'):\n",
    "    gbdt_model = results['梯度提升树']['model']\n",
    "    gbdt_importances = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '重要性': gbdt_model.feature_importances_\n",
    "    }).sort_values('重要性', ascending=False)\n",
    "    \n",
    "    print(\"\\n梯度提升树特征重要性:\")\n",
    "    print(gbdt_importances)\n",
    "    \n",
    "    # 可视化\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    plt.barh(gbdt_importances['特征名称'], gbdt_importances['重要性'])\n",
    "    plt.xlabel('特征重要性')\n",
    "    plt.title('梯度提升树特征重要性')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 线性回归系数分析\n",
    "if hasattr(results['线性回归']['model'], 'coef_'):\n",
    "    lr_model = results['线性回归']['model']\n",
    "    lr_coefficients = pd.DataFrame({\n",
    "        '特征名称': X.columns,\n",
    "        '系数': lr_model.coef_,\n",
    "        '系数绝对值': abs(lr_model.coef_)\n",
    "    }).sort_values('系数绝对值', ascending=False)\n",
    "    \n",
    "    print(\"\\n线性回归系数分析:\")\n",
    "    print(lr_coefficients)\n",
    "    \n",
    "    # 可视化\n",
    "    plt.figure(figsize=(10, 6))\n",
    "    top_coeff = lr_coefficients.head(10).sort_values('系数', ascending=True)\n",
    "    plt.barh(top_coeff['特征名称'], top_coeff['系数'])\n",
    "    plt.xlabel('系数值')\n",
    "    plt.title('线性回归系数分析 - 前10特征')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 10.选择最佳模型\n",
    "best_model_name = performance_df.iloc[0]['模型']\n",
    "best_model = results[best_model_name]['model']\n",
    "best_r2 = performance_df.iloc[0]['R²分数']\n",
    "\n",
    "print(f\"\\n\" + \"=\"*50)\n",
    "print(f\"最佳模型: {best_model_name}\")\n",
    "print(f\"最佳R²分数: {best_r2:.4f}\")\n",
    "print(\"=\"*50)\n",
    "\n",
    "# 使用最佳模型进行预测示例\n",
    "best_y_pred = best_model.predict(X_test)\n",
    "print(\"最佳模型的前10个预测结果:\")\n",
    "comparison_df = pd.DataFrame({\n",
    "    '实际价格': y_test.values[:10],\n",
    "    '预测价格': best_y_pred[:10],\n",
    "    '绝对误差': abs(y_test.values[:10] - best_y_pred[:10])\n",
    "})\n",
    "print(comparison_df)\n",
    "\n",
    "# 11.残差分析\n",
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "# 残差图\n",
    "plt.subplot(1, 2, 1)\n",
    "residuals = y_test - best_y_pred\n",
    "plt.scatter(best_y_pred, residuals, alpha=0.6)\n",
    "plt.axhline(y=0, color='red', linestyle='--')\n",
    "plt.xlabel('预测值')\n",
    "plt.ylabel('残差')\n",
    "plt.title(f'{best_model_name} - 残差图')\n",
    "\n",
    "# 预测分布\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(y_test, alpha=0.7, label='实际值', bins=20)\n",
    "plt.hist(best_y_pred, alpha=0.7, label='预测值', bins=20)\n",
    "plt.xlabel('价格')\n",
    "plt.ylabel('频数')\n",
    "plt.title('实际值 vs 预测值分布')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ce98e119",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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